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Open Access 2024 | Open Access | Book

16th International Symposium on Advanced Vehicle Control

Proceedings of AVEC’24 – Society of Automotive Engineers of Japan

Editors: Giampiero Mastinu, Francesco Braghin, Federico Cheli, Matteo Corno, Sergio M. Savaresi

Publisher: Springer Nature Switzerland

Book Series : Lecture Notes in Mechanical Engineering

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About this book

This open access book highlights the latest advances, innovations, and applications in the field of vehicle systems dynamics and control, as presented by leading international researchers at the 16th JSAE International Symposium on Advanced Vehicle Control (AVEC), held at Politecnico di Milano, Milan, Italy, on September 2-6, 2024. It covers a diverse range of topics such as vehicle dynamics theory, steering, brake, tire, suspension, chassis control, powertrain, electrified vehicles, stability enhancement systems, driver-vehicle systems, advanced driver assistance systems and automated driving systems, driving simulator dynamics and control. The contributions, which were selected by means of a rigorous international peer-review process, present a wealth of exciting ideas that will open novel research directions and foster multidisciplinary collaboration among different specialists.

Table of Contents

Frontmatter

Open Access

Safe Control Allocation of Articulated Heavy Vehicles Using Machine Learning

As articulated heavy vehicles are over-actuated, achieving a safe control allocation is crucial to ensure stability. This study introduces a machine learning model developed to identify unsafe behaviours and modes, such as jack-knifing and trailer swing, enabling the control scheme to prioritize stability. High-fidelity simulations, focusing on high-risk scenarios, generate data for training the machine learning model. This model is integrated into the control scheme to predict safe braking allocations and prevent unsafe vehicle modes during real-time driving scenarios. Initial tests showed promising results regarding prediction accuracy and a safety margin that can be implemented to further ensure that safe vehicle motion is achieved.

Sander van Dam, Lukas Wisell, Kartik Shingade, Mikael Kieu, Umur Erdinc, Maliheh Sadeghi Kati, Esteban Gelso, Dhasarathy Parthasarathy

Open Access

Trajectory Tracking for High-Performance Autonomous Vehicles with Real-Time Model Predictive Control

This work is the development of a Model Predictive Controller (MPC) for the integrated control of lateral and longitudinal dynamics of a high-performance autonomous car, which follows a given trajectory on a racetrack. The MPC model is based on an Affine-Force-Input single-track nonlinear bicycle model that accounts for actuation dynamics and delays. The MPC problem is formulated as a quadratic problem, enabling efficient real-time solution with a specific quadratic programming (QP) solver. The controller is implemented in and tested in a real-time hardware-in-the-loop (HIL) simulator, showing excellent tracking performance up to 280 km/h.

Matteo Pierini, Paolo Fusco, Rodrigo Senofieni, Matteo Corno, Giulio Panzani, Sergio Matteo Savaresi

Open Access

Study on Linear Actuators for Intake and Exhaust Systems of Internal Combustion Engines (Analytical Consideration of Thrust Characteristics by Permanent Magnet Array)

To enhance internal combustion engine performance, this study focuses on developing an electric valve drive system utilizing linear actuators for intake and exhaust valve control. The linear actuator, comprising a movable coil and a fixed permanent magnet, operates based on the principle of the Lorentz force. Unlike traditional magnetic circuits, this actuator employs five permanent magnets with different magnetization directions to concentrate the flux on the coil. In this study, multiple models with varying ratios of these permanent magnets were created and analyzed using finite element analysis conducted with the JMAG software to investigate the thrust characteristics during the reciprocating motion of the actuator. The vector plot of the magnetic flux density shows that the magnetic circuit is predominantly composed of permanent magnets. The average thrust at a 10 mm displacement was approximately 107 N in the largest model. Future studies will aim to design actuators with increased thrust capabilities.

Jumpei Kuroda, Kaito Kimura, Ryutaro Ono, Ikkei Kobayashi, Daigo Uchino, Kazuki Ogawa, Keigo Ikeda, Ayato Endo, Hideaki Kato, Takayoshi Narita

Open Access

Drifting Maneuver Investigation via Phase Plane Analysis of Experimental Data

This paper presents a comprehensive analysis of experimental data on drifting maneuvers, using vehicle data collected by Stanford University with a professional driver. Vehicle dynamics during drifting, characterised by high sideslip angles and countersteering, are examined. By using a nonlinear single track model with nonlinear tires, this study compares real-car data to simulated models within a phase-plane framework. It also explores the application of saddle-node bifurcation theory to understand the abrupt changes in vehicle behaviour during drifting.

Giovanni Righetti, Guido Napolitano dell’Annunziata, Flavio Farroni, Matteo Massaro, Basilio Lenzo

Open Access

Effects of External Tire Heating on Rolling Resistance Energy Consumption

It is known that the rolling resistance decreases with increasing tire temperature. If the tires could be heated to a high temperature, the rolling resistance’s energy loss could be reduced. The question arises whether the reduced rolling resistance energy consumption overcomes the energy required to heat the tire.This paper investigates the effects of external heating and improved tire insulation theoretically. The results indicate that external tire heating can be beneficial only if the heat used is waste heat, generated from a heat pump or similar with a coefficient of performance greater than one or taken from the grid.

Mikael Askerdal, Fredrik Bruzelius, Jonas Fredriksson

Open Access

Bifurcation Analysis of a Nonlinear Vehicle Model on Banked Road

Towards the transition to automated driving, lateral stability of the vehicle represents a key requirement to guarantee the safety of passengers and vulnerable road users, especially during emergency operating conditions where nonlinearities arise. The present paper aims at investigating the effect of road banking angle on vehicle plane motion stability. To perform this analysis, a pure lateral nonlinear double track model is numerically derived for an oversteering vehicle. Lateral load transfer and its distribution among the axles are included for exploiting the tyre saturation region. The stability analysis is conducted by searching for the vehicle steady-state conditions and deriving the linearised equations around the equilibrium points. Moreover, the phase-plane plot is adopted to draw the states trajectories and to identify potential unstable regions. Finally, the bifurcation analysis as function of the road banking angle is investigated to highlight possible change of the phase portrait topology. The results show that a saddle-node bifurcation may occur when the vehicle is negotiating a certain level of bank road angle, affecting the vehicle yaw stability region.

Luca Zerbato, Enrico Galvagno, Mauro Velardocchia

Open Access

Regenerative Brake Blending in Electric Hypercars: Benchmarking and Implementation

This paper presents a novel Regenerative Brake Blending (RBB) strategy for an electric hypercar, framing it as a multi-objective problem. These include thermal and lifespan management of various components plus maximizing energy recovery. We begin by mathematically modelling each subsystem of the RBB layout. Then, we define an acausal offline optimal control problem to establish a benchmark solution; subsequently we propose a causal real-time control strategy inspired by the Equivalent Consumption Minimization Strategy (ECMS). The proposed real-time strategy shows a performance loss of 1.6% compared to the benchmark underscoring the efficacy of the proposed RBB strategy in maximizing energy recuperation while considering for brake temperatures.

Rodrigo Senofieni, Federico Bassi, Matteo Corno, Sergio Matteo Savaresi, Gianluca Savaia

Open Access

A Method for Obtaining Reference Friction Values for Validation of Road Friction Estimation Algorithms

Data-driven development of friction estimators for passenger vehicles is becoming popular. They rely mainly on training data to obtain an accurate estimate of the current road conditions. However, reference or training data for natural conditions containing available friction is sparse. This limits the development of data-driven approaches for friction estimation. The current paper presents progress in a project devoted to developing a method to use standard equipment for road monitoring to acquire reference data for friction estimation, relevant to specific tyres and operating conditions. Results show how a mapping between existing test equipment readings and the real experienced coefficient of friction of a car tyre can be made.

Mattias Hjort, Fredrik Bruzelius, Sogol Kharrazi, Derong Yang

Open Access

Computing the Lateral Compliance of the Racing Line Using Trajectory Optimization

While race car drivers speak often on racing lines, data reveals they do not follow consistent paths lap over lap. Furthermore, autonomous racing controllers designed to track optimal paths fail to keep pace with the best professional drivers. In this paper, we assess the importance of the racing line from an optimization perspective by evaluating its dynamic sensitivity to lateral perturbations. After applying the method to the Laguna Seca Raceway, we find that there is in fact a family of trajectories that are significantly different in path but similar in lap time. This finding is consistent with prior experimental work and indicates that extracting the peak dynamic performance from a vehicle may require reasoning through this whole family of solutions—instead of tracking a single line—to operate the vehicle at its true limits.

R. K. Aggarwal, J. C. Gerdes

Open Access

State Estimation and Sensorimotor Noise in a Driver Steering Model with a Gaussian Process Internal Model

Refinements to a mathematical model of human drivers’ steering control incorporating driver learning are reported. State estimation and realistic sensorimotor noise sources are introduced to the driver model to better represent neural processes. It is found that the driver model exhibits the expected learning behaviour in terms of estimation and control performance. Further work is planned to validate the model experimentally.

Harry Fieldhouse, David Cole

Open Access

Energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles

The four-wheel independent steering and drive autonomous vehicle is a typical over-actuated system. The complexity of controlling it is increasing with the number of actuators. Since the model-based approach can solve the constrained multiple output problem, it is mostly utilized in the existing works. However, they usually investigate a single objective optimization, while employing simplified prediction models to relieve computational burdens. In this case, the robustness of the controller will inevitably suffer from model mismatch, which makes it hard to fulfill the various demands of autonomous driving. This work proposes a multi-objective control framework, which optimizes stability and energy efficiency simultaneously. Furthermore, robust model predictive control is introduced to address the model mismatch. Compared with the state-of-the-art, the effectiveness of the proposed approach has been validated by hardware-in-the-loop tests. Under the double lane change Maneuver, the longitudinal speed is maintained 1.7% higher. The vehicle stability is enhanced, while the motor energy loss and tire slip energy are reduced by 23.3% and 8.3%, respectively.

Ziang Tian, Huilong Yu, Junqiang Xi

Open Access

Profile Generation of Cooperative Driving in Urban Intersections for Energetic Evaluation

In the context of urban smart mobility, automated vehicles communicating with each other, surrounding infrastructure, and other traffic participants yield the use for cooperative driving in urban environments. This, alongside an increase in safety and comfort, can help to reduce fuel and energy consumption individually and with regard to the cooperative vehicle cluster. In this paper we are addressing the impact urban cooperative driving has on the energy consumption of electric vehicles and further investigate the impact on drive systems and their layout, as well as the impact different control strategies of the cooperative network and scenarios with varying vehicles densities have. In order to evaluate the impact, defined driving profiles from a graph based optimization for cooperation networks are evaluated with regard to their energy consumption. Based upon the results the overall energetic impact of different intersection approaching strategies as well as the drive system impact are discussed.

Maximilian Flormann, Axel Sturm, Roman Henze, Pongsathorn Raksincharoensak

Open Access

Energetic Impact of Urban Cooperative Driving on the Example of Intersections

The use of automated driving and connectivity can be an additional lever to reduce fuel and energy consumption in real driving and has an impact on the drive system and its dimensioning as well. In this paper we are addressing the impact urban cooperative driving on the energy consumption of electric vehicles and investigate further the impact of the drive system and their layout. To this end, a concept study is being conducted for a D-segment vehicle and two battery electric powertrains. In order to evaluate the impact, defined driving profiles from a connected intersection are used for simulation. Based upon the results the overall energetic impact of different intersection approaching strategies as well as the drive system impact are discussed. It showed that a cooperative intersection scenario leads to an energy reduction of 14% and first come first serve scenarios to an energy reduction of 7%.

Axel Sturm, Maximilian Flormann, Roman Henze, Pongsathorn Raksincharoensak

Open Access

Speed Control in the Presence of Road Obstacles: A Comparison of Model Predictive Control and Reinforcement Learning

The paper compares two optimal control methods — Reinforcement Learning and Model Predictive Control — for adaptive speed control in the presence of road obstacles to enhance ride comfort. Both methods use a model for training or prediction and a reward or cost function to achieve a desired control objective. Using the same quarter-car model and objective function for both methods, differences in planned speed profiles, optimality of the control objective, and differences in computational time are analysed through simulations over a series of cosine-shaped road bumps.

P. Mandl, F. Jaumann, M. Unterreiner, T. Gräber, F. Klinger, J. Edelmann, M. Plöchl

Open Access

A Geometric Electric Motor Model for Optimal Vehicle Family Design

This paper presents a design optimization framework that jointly optimizes battery size with the geometric dimensions of the electric motor for a family of battery electric vehicles, with global optimality guarantees. As opposed to conventional models, we devise a quasi-static model of the motor internal losses as a function of both its geometry and operating points, using a convex surrogate modeling approach. Specifically, we implement a low-level motor scaling, capturing the impact on performance and losses of changing the motor geometry in axial and radial directions. Hence, we leverage the framework to solve a concurrent optimization problem and identify the optimal module sizing for a family of electric vehicles. Finally, we test our framework on a benchmark problem where we jointly design motor and battery for three different types of vehicles (a city car, a compact car, and a cross over), whereby the prediction efficiency is in line with the high-fidelity modeling software.

Maurizio Clemente, Olaf Borsboom, Mauro Salazar, Theo Hofman

Open Access

Road Friction Adaptive Lateral Control of Automated Vehicles with Differential Braking

Differential braking offers a promising approach to ensure lateral control of automated vehicles in the event of steering actuator failure. Braking interventions induce longitudinal forces on the wheel, which influence the vehicle’s yaw motion and thus enable lateral control along a given trajectory. Within the scope of this paper, a road friction adaptive system for lateral vehicle control by differential braking on a dry, wet, snow- and ice-covered road is derived simulatively. The investigated friction coefficient adaption is implemented by a variable path planning and calculated on the basis of an estimated road friction coefficient. The driving situation considered is based on the severe lane-change maneuver according to ISO 3888-2 for the investigation of vehicle dynamics and road-holding ability. The limitation of the trajectory results from the maximum yaw rate and expands the trajectory’s length for the different road friction coefficients.

Jannes Iatropoulos, Tim Ahrenhold, Leon Salzwedel, Roman Henze

Open Access

Model-Free Automated Reversing of Articulated Heavy Goods Vehicles

This paper presents a technique for automated reversing control of articulated vehicles. Reversing articulated Heavy Goods Vehicles (HGVs) can be a challenging and time consuming task for a human driver, sometimes requiring multiple forward and backward motions to reduce errors. Here, the aim is to automate the task to provide high levels of precision using Artificial Flow Guidance (AFG). AFG uses simple geometry to define a spatially distributed motion reference, requiring only short-range error corrections and possessing global convergence properties. AFG has previously been applied to rigid and articulated vehicles in forward motion, with demonstrable benefits in terms of tracking precision and robustness. Here results focus on the tractor-semitrailer, but the AFG approach is equally applicable to the reversing of longer combination vehicles.

Shammi Rahman, Timothy Gordon, Leon Henderson, Yangyan Gao, Sonya Coleman, Dermot Kerr

Open Access

Nonlinear Dynamics of a Controlled Two-Wheeled Trailer

The nonlinear dynamics of towed two-wheeled trailers is investigated using a spatial, 4-DoF model. Namely, the yaw, pitch, and roll motions are all taken into account. Geometrical nonlinearities and the non-smooth characteristics of the tire forces are considered. A linear state feedback controller with feedback delay is designed to enhance the stability performance of the trailer. Numerical bifurcation analysis is performed to investigate the large amplitude vibrations and unsafe (bistable) zones, where the stable rectilinear motion and the stable limit cycle coexist with each other. The effects of the control gain and the feedback delay of the controller are presented on bifurcation diagrams. It is shown, that with appropriately chosen control gains, the size of the bistable region can be limited.

Hanna Zsofia Horvath, Adam Balint Feher, Denes Takacs

Open Access

Yaw Stability Control of Vehicles Using a Slip Polytope Validated with Real Tests

Articulated heavy vehicles (AHVs) face yaw instabilities, especially under extensive propulsion or regenerative braking force on the driven axles, risking their directional stability and potentially leading to jackknifing. Hence, safe operating envelopes (SOEs) are essential for allocating propulsion and braking forces among different units. This study proposes a novel approach to ensure yaw stability by reducing longitudinal slip limits of the electric motors (EMs) based on side-slip, enhancing stability and acceleration performance. Validation through simulations and real vehicle tests shows promising results.

Umur Erdinc, Mats Jonasson, Maliheh Sadeghi Kati, Leo Laine, Bengt Jacobson, Jonas Fredriksson

Open Access

Study on Accident-Avoidance Mechanism in Driver-Vehicle System When Activating Level-2 ADAS

Advanced Driver Assistance Systems (ADAS) in passenger cars, such as Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), have recently been widely deployed. However, these systems are not designed for high-risk situations, yet some drivers over trust the system and engage in secondary tasks. These behaviours may lead to serious accidents. On the other hand, it has been reported that the use of ACC reduced approximately half of the collision rate on highways based on public road data. This study aims to clarify the mechanism how ADAS affects driver behaviour and enhances accident prevention performance. Specifically, the focus is on the forward collision risk and the driver’s brake operating behaviour. Through the analysis using a driving simulator, this study compared driver behaviour in high collision risk situations in the case with and without ACC. The analysis of subject drivers’ internal parameters confirmed the driver behaviour that avoided a collision by using ADAS to take over control and brake earlier.

Norika Arai, Jinnosuke Kamimura, Yohei Fujinami, Xingguo Zhang, Pongsathorn Raksincharoensak, Masaaki Uechi, Shintaro Inoue, Fumio Sugaya, Kazunori Nogi, Toshinori Okita, Hideaki Hayashi, Keitaro Niki

Open Access

Motion Control of a 6 4 Heavy Vehicle: Autonomous Collision Avoidance Using Integrated Chassis Control

This paper considers the coordinated chassis control of a 6 $$\times $$ × 4 HGV tractor unit using a multivariable nonlinear controller for a transient handling manoeuvre under friction-limited conditions. The controller’s performance is evaluated through simulation. It receives Centre of Gravity (CG) longitudinal and lateral acceleration targets, corresponding to curvature and longitudinal acceleration requests, and aims for the CG to track the target accelerations. It employs the Modified Hamiltonian Algorithm (MHA) to generate steering and braking commands for the tractor. A combined-slip Magic Formula tyre model used within the algorithm allows for simultaneous stability control and path tracking, even in scenarios where the vehicle is operating at the limits of tyre adhesion. The manoeuvre is an autonomous obstacle avoidance on a packed snow. Results show the advantages and possible limitations of tracking acceleration targets for integrated chassis control.

Aria Noori Asiabar, Timothy Gordon, Yangyan Gao, Leon Henderson, Leo Laine

Open Access

Investigating Characteristics and Opportunities for Rear-Wheel Steering

The potential of additional steering possibilities (like rear-wheel or all-wheel steering) is analyzed for critical situations to investigate possible safety improvements. For this purpose, a dynamic optimization problem is formulated to find the best possible maneuver. The optimization criterion is to maximize the entry speed into a constant radius $$90^\circ $$ 90 ∘ -curve. The optimization problem is solved for different steering topologies, and the results quantify the increase in maximum entry speed, highlighting the potential for safety improvements. Further, the optimal steering strategies are determined, and they show interesting characteristics like initial diagonal driving or, in other cases, a transition from initial out-of-phase steering to in-phase steering.

Oskar Lind Jonsson, Arvind Balachandran, Jian Zhou, Björn Olofsson, Lars Nielsen

Open Access

Accuracy Requirements of Camera-Based Depth Estimation for Urban Automated Driving

For autonomous driving in urban areas higher accuracy requirements for localization of surrounding traffic participants become apparent. The use of cost-efficient camera sensors shows potential for a performant depth estimation and can supplement perception systems to achieve redundancy. Current research focuses on improving the algorithms towards better performance whereas the application-oriented analysis of present estimation errors in relation to urban traffic scenarios is often neglected. Based on stereo and mono camera images, a benchmark analysis of rule- and deep learning-based depth estimation approaches is conducted in this work. The error-prone estimation results are then analyzed against braking distances of urban traffic scenarios simulated by a two-track model to analyze the criticality of different depth estimation approaches. The application-oriented evaluation shows that current approaches could already be used in real automated driving systems and enable the definition of requirements.

M. Westendorf, S. Thal, T. Ahrenhold, R. Henze

Open Access

STS-GAN: Spatial-Temporal Attention Guided Social GAN for Vehicle Trajectory Prediction

Accurately predicting the trajectories of other vehicles is crucial for autonomous driving to ensure driving safety and efficiency. Recently, deep learning techniques have been extensively employed for trajectory prediction, resulting in significant advancements in predictive accuracy. However, existing studies often fail to explicitly distinguish the impact of historical inputs at different time steps and the influence of surrounding vehicles at distinct locations. Moreover, deep learning-based approaches generally lack model interpretation. To overcome the issues, we propose the Spatial-Temporal Attention Guided Social GAN (STS-GAN). In the generator, we proposed a spatial-temporal attention mechanism to guide the utilization of trajectory features and interaction of the target vehicle with its surrounding vehicles. The spatial attention mechanism evaluates the importance of surrounding vehicles for predictions of the target vehicle, while the temporal attention mechanism learns the significance of historical trajectory information at different historical time steps, thereby enhancing the model interpretation. A convolutional social pooling module is employed to capture interaction features from surrounding vehicles, which are subsequently fused with the attributes of the target vehicle. Experimental results demonstrate that our model achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

Yanbo Chen, Huilong Yu, Junqiang Xi

Open Access

Automated Parking System for Tractor-Trailer Vehicle

By virtue of recent development in automated parking system, there has been growing interest in tractor-trailer automated parking application (TTAPA). However, there are two major obstacles when developing TTAPA. The one is low maneuverability of reversing a tractor-trailer vehicle (TTV), caused by hitch angle steering characteristic, and the other is heavy computational cost from non-linearity of model. This paper considers an autonomous parking system (APS) for a TTV. To achieve successful autonomous parking under various circumstances, this system adopts nonlinear model predictive control and LQR as path planner and path tracking controller, respectively. To validate the proposed APS for TTV, the system is implemented and evaluated in MATLAB/Simulink and TruckSim co-simulation environment. From simulation results, it is shown that the proposed APS can achieve parking scenario goal with good performance for a TTV.

Jinwoo Kim, Seongjin Yim

Open Access

Research on Jerk Reduction Route Planning Method for Autonomous Driving Using Vehicle Forward Information

We propose a forward-gazing model for path planning (Preview Path-Planning model) as the most basic model that provides insight to people who share the paradigm of human-like path-planning modeling. The basic concept is to apply a low-pass filter to changes in forward curvature. In addition, the phase delay due to the low-pass filter is compensated by forward gaze. We also report the results of determining the longitudinal motion by applying the GVC technology to the situation in which the vehicle could run perfectly along the path.

Takumi Komiya, Masato Abe, Yoshio Kano, Makoto Yamakado, Yu Sato, Ken-taro Ueno, Yusuke Tanaka

Open Access

Online Motion Planning for All-Wheel Drive Autonomous Race Cars

The advent of autonomous racing events, such as Formula Student Driverless Cup, requires online motion planning algorithms that push the vehicle to its limits while ensuring vehicle stability and preventing road departure. A popular method to find the optimal control input to drive at the limits of the car is Nonlinear Model Predictive Control (NMPC). However, when NMPC is used, often a trade-off has to be made between performance, accuracy, and computational complexity. In this manuscript, the principle of cascading different vehicle models is used to construct the prediction horizon. Initially, a two-track model optimizes steering and motor input, utilizing torque vectoring benefits. The horizon is then extended with a single-track model, and a lower fidelity point mass model, effectively reducing computational complexity. Furthermore, by adopting a curvilinear reference frame, a transformation towards the spatial domain is obtained, which allows us to use time as an optimization variable. A simulation study is performed for varying prediction horizon lengths which show the advantages of the cascaded vehicle model, achieving an 86% reduction in computation time with comparable lap times.

Mischa Huisman, Erjen Lefeber

Open Access

A Data-Driven Framework for Tire Force Estimation of Distributed Electric-Drive Vehicles

In recent years, with the development of wheel-side motors and hub motors, distributed electric drive vehicles, gradually enter the electric vehicle market.Tire force are often derived from rule-based model in the past. However, distributed electric drive vehicles have a higher degree of freedom put forward new control requirements. This puts forward higher requirements for the accuracy of the tire force model. Rule-based model cannot meet the requirements quite well. Because of this, our study established a tire force residual correction framework for distributed electric drive vehicles. The framework consists of a neural network model (MLP, MLP-seq, and MLP-mixer) and a physical rule-based model. The framework was proved in the study to output a more accurate force estimation which will help dynamic modeling and control tasks.

Rujun Yan, Kun Jiang, Bowei Zhang, Jinyu Miao, Diange Yang

Open Access

Influence of the Front-Rear Torque Distribution on the Handling Characteristics and Stability Boundaries of an AWD-Vehicle

The influence of the drive torque distribution of an AWD vehicle with individual motors at the front and rear axles on the handling and stability properties is investigated. By applying bifurcation analysis methods, different types of loss of stability at combined longitudinal and lateral acceleration are identified. The impact of the drive torque distribution on the stability boundaries in the GG diagram is examined, and the related stable acceleration envelope is compared to the envelope derived from applying optimisation methods. Representative corresponding handling characteristics are compared and discussed.

Manuel Eberhart, Martin Arndt, Johannes Edelmann, Manfred Plöchl

Open Access

Determination of the Loss Behavior of Wheel Bearings Under Real Driving Conditions

The characterization of component losses is typically conducted on dedicated test benches with the objective of enhancing component efficiency. Nevertheless, obtaining precise measurements of the actual loss contributions of components during real-world vehicle operations is often challenging. This challenge particularly pertains to wheel bearings, whose loss characteristics are efficiently delineated through the generation of efficiency maps on test benches at the Institute of Automotive Engineering. This submission presents a methodology for establishing these efficiency maps and introduces a developed methodology that enables the transfer of loss behavior from the component test into real-world driving conditions. Subsequently, the power losses are quantified across distinct driving domains, including urban, rural, and highway conditions. The outcomes of this methodology are contrasted with the losses observed on the test benches in order to identify potential avenues for reducing the losses of wheel bearings. Furthermore, the methodology is applied to homologation-relevant vehicle test benches with the objective of comparing the power losses under these conditions with real-world vehicle operations.

Lukas Hartmann, Leon Ohms, Ron Rebesberger, Gerrit Brandes, Roman Henze

Open Access

Prediction Based Cooperative Adaptive Cruise Control for Heterogeneous Platoons with Delays

We present a prediction based controller for heterogeneous platoons with actuation delay. By using a prediction of the ego vehicle’s acceleration and compensating the ego vehicle’s influence on the error dynamics, we obtain a controller that achieves input-to-state stability (ISS) with respect to the preceding vehicle’s acceleration. The result is a controller that does not require driveline information of the preceding vehicle, which enables platooning in a heterogeneous setting. An analysis is presented of the string stability properties of the system with both actuation and communication delays. The effectiveness of the controller is shown in simulation.

Redmer de Haan, Tom van der Sande, Erjen Lefeber

Open Access

Vertical Dynamics Control Using Active Tires and Preview

In this paper, the effects of adding a tire actuator to a vehicle with active suspension and preview control are studied. For the controller, the LQR method is used. The dual actuator system with preview control is compared to other conventional systems to determine the performance gain. The results show a gain of 182.3% in comfort compared to the passive suspension. The dual actuator system with preview control outperforms systems with fewer actuators and is less affected by the trade-off between handling and comfort.

Tom van der Sande, Nick te Kronnie

Open Access

Time-Optimal Learning-Based LTV-MPC for Autonomous Racing

Autonomous racing is a time- and accuracy-critical application of vehicle motion planning and control techniques. Despite being promising for its ability to handle constraints, model predictive control (MPC) for autonomous racing is limited by the relatively low computational speed and the problem of model mismatch. In this work, we present a time-optimal linear-time-variant-MPC (LTV-MPC) that incorporates a min-time objective function, the friction ellipse constraint, and the successive linearization over the prediction horizon to improve computational speed and prediction accuracy. To tackle model mismatch, the proposed LTV-MPC is further combined with Gaussian process regression to learn the lateral tire force error. Compensation for the error is implemented over the prediction horizon and on the friction ellipse constraint. This work presents simulation validation on the racing track of Formula Student Autonomous China (FSAC) and experimental validation on a self-designed track. We show that compared with nonlinear MPC, the proposed LTV-MPC reduces the average computation time from 66 ms to 2.5 ms with a 0.6% increase in lap time. With learned tire force error, a 2% reduction in lap time can be achieved.

Zijun Guo, Huilong Yu, Junqiang Xi

Open Access

Speed Profile Definition for GLOSA Implementation on Buses Based on Statistical Analysis of Experimental Data

The latest advancements in vehicle automation have revealed significant potential for enhancing traffic management via Advanced Driver Assist Systems (ADAS), benefiting both safety and environmental considerations. Green Light Optimal Speed Advisory (GLOSA) systems represent a significant application in the Cooperative-Intelligent Transportation System (C-ITS) field adopting Vehicle-to-Everything (V2X) communication technology. The literature nowadays addresses quite extensively the GLOSA, and C-ITS in general, for conventional vehicles like cars. At the same time, there is emerging research starting to involve also the public transportation vehicles within this framework. The focus for buses is typically posed either on the comfort and regularity of the service for passengers or on the energy consumption which is reduced by adopting suitable speed profiles for the vehicle, thus avoiding unnecessary stops. This work presents a statistical analysis based on experimental data collected in real-world urban scenarios over one entire year. The outcome of this analysis allows the design of speed profiles typical for a public transportation vehicle, accounting for features such as the bus stop station for getting passengers off and on.

Daniele Vignarca, Stefano Arrigoni, Edoardo Sabbioni, Federico Cheli

Open Access

Longitudinal Control Concept for Automated Vehicles in Stop-and-Go Situations

This paper presents a longitudinal control concept for automated vehicles in stop-and-go situations that enables the optimization of driving behavior in terms of occupant comfort and traffic efficiency. In a simulative validation based on real driving data, different variants are evaluated on the basis of objective criteria and an optimal functional behavior is derived.

C. Pethe, M. Heinze, M. Flormann, R. Henze

Open Access

Inverse Approach to Vehicle Generalized Parameters for Individual Drives

In this paper, a method is presented to determine the individual tire slippages and wheel circumferential forces/torques for vehicles with individually-driven wheels. The method is based on a system of parameters of individual wheels mathematically linked to the vehicle generalized parameters. The paper demonstrates an inverse approach on how the generalized parameters can be used to determine individual wheel parameters when the vehicle generalized parameters are given for a single, generalized wheel, whose kinematics and dynamics are equivalent to those of the vehicle. The method can be applied to improve terrain mobility and energy efficiency of autonomous ground vehicles, including planet rovers whose guidance systems do not take into consideration that the tire slippages and circumferential forces are different or should be different by determining individual parameters of the e-motors to provide maximum mobility or energy efficiency in a straight line motion.

Vladimir Vantsevich, Jesse Paldan, David Gorsich, Lee Moradi

Open Access

Effect of Driving Force Control that Imitates the Function of Tire Lateral Force on Vehicle Dynamics

Various control methods have been proposed that aim to improve steering response, disturbance stability, and stabilize steering characteristics by distributing drive force to each wheel. Conventionally, controls have been constructed by combining those control methods and tuning gains for each control. However, such control design methods require a huge amount of man-hours, and besides, it is not clear which states are optimal. Therefore, in this paper, we first focused on the mechanism of tire lateral force. In general, vehicles can run stably against disturbances without implementing any feedback control. It was clarified that this is because the tire lateral force plays the role of a skyhook damper installed horizontally on the side of the vehicle. Therefore, we proposed a control method that brings out the maximum potential of tires based on the idea that tire longitudinal force should have the same function as tire lateral force.

Etsuo Katsuyama

Open Access

Vibration Suppression of Automotive Drivetrains Based on Tire-Speed Observer and Backlash Compensation

This paper proposes a novel vibration suppression control method, which suppresses the torsional resonance of the drive shaft caused by motor torque change in the general drivetrain structure of automobiles. For realizing the method in the simplest and most prospective way, the proposed method calculates the feedback torque from the difference between the motor speed and the tire speed multiplied by a proportional gain, with estimating the tire rotation speed by an observer. In addition, a backlash compensation method is developed to reduce noise and vibration in gear retightening in the backlash region. The method observes the fluctuation of the motor speed after the torque zero-crossing and determine the motor torque so that the motor rotation speed returns to the speed at the entrance of the backlash. The effectiveness of the proposed methods is verified by simulation and driving test on the experimental vehicle.

Kenta Maeda, Naoki Shinohara, Satoshi Kaneko, Hiroki Sonoda

Open Access

Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty

This paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.

Junyeong Seong, Sungjun Park, Kunsoo Huh

Open Access

A Parametric Interpolation-Based Approach to Sideslip Angle Estimation

Vehicle sideslip angle has always been of interest for stability controls enhancing vehicle safety. As well-known, measuring sideslip is impractical and expensive, motivating techniques to estimate it using already-available vehicle sensors. This paper proposes a new methodology to estimate sideslip angle, separating kinematic and dynamic sideslip angle contributions with the idea that the former is straightforward and the latter may be obtained with a lateral-acceleration-based interpolation. The proposed approach is validated through experimental data on a passenger vehicle.

Mariagrazia Tristano, Basilio Lenzo

Open Access

Optimal Path Generator Embedded Model Predictive Control for Automated Vehicles

This paper investigates real-time optimal control for an automated vehicle. The model predictive control that generates the optimal trajectories has found wide applications in recent years due to increased computational performance. Numerical simulations of the full-vehicle model investigate the applicability of a path generator embedded model predictive control for the vehicle in the general shape road. The path generator is constructed by deep learning using the multiple open-loop optimal control problem solutions as the training dataset. Results demonstrate that the sequentially calculating optimal control command has the potential for real-time optimal control in the presence of the obstacle.

Takashi Sago, Yoshihide Arai, Yuki Ueyama, Masanori Harada

Open Access

Development and Experimental Assessment of a Control Logic for Hydroplaning Prevention

Hydroplaning plays a crucial role in road safety. The water layer wedged between the tires and the road reduces the capability of the vehicle to respond to the driver’s inputs. Factors like vehicle speed, water layer thickness, tread pattern, and tire wear affect hydroplaning onset. Based on the technical literature and experimental tests, the research developed a tire model to simulate hydroplaning effects. Scaling factors were added to the MFTyre model to reproduce the changes in cornering stiffness, relaxation length, friction coefficient, and motion resistance. Then, a control logic to counteract hydroplaning was designed and implemented on a 14-dof vehicle model. As last, the effectiveness of the control logic was assessed through a series of indoor tests on a dynamic driving simulator.

Edoardo Montini, Marco Salierno, Stefano Frigerio, Stefano Melzi

Open Access

Charging Stations for Electric Vehicles in SUMO Simulation Environment and Their Impact on the Traffic Flow

Planning of charging stations is becoming increasingly prevalent. Consequently, it is of paramount importance to assess their impact on traffic flow and, subsequently, their optimal positioning. To identify these optimal positions, microscopic traffic flow simulations represent an efficacious tool. Different positions can be trialed until an optimal solution is identified. The open-source microscopic traffic flow simulation tool SUMO would be an optimal choice for this purpose, but does not yet offer the possibility of efficiently implementing charging stations in its traffic networks. As part of a current research project, a novel method for implementing charging stations at their real location within the SUMO environment is presented. Exemplary locations are selected in the German city of Essen.

Eva Spachtholz, Marvin Glomsda, Ingmar Kranefeld, Frédéric Etienne Kracht, Dieter Schramm

Open Access

Primary and Secondary Vehicle Lightweighting Achieved by Acting on the Battery Thermal Management System

Global warming and air pollution are the main factors influencing international, national, and local strategies for the transition towards clean technologies to reduce polluting and climate-altering emissions. A further reduction of the latter can be achieved, with the same powertrain technology, by reducing vehicle consumption. One technique is to lighten the vehicle. The goal of this feasibility study is to act on the battery thermal management system to achieve vehicle lightweighting. Specifically, a sedan car with active-cooled batteries was considered as a reference case, and primary lightweighting was achieved through the use of passive cooling methods, i.e., air and Phase Change Material (PCM) cooling systems, followed by secondary lightweighting to re-establish the target range of the reference vehicle by downsizing the batteries. The air-cooled system leads to greater lightweighting, but its field of application is limited to vehicles operating in fleets; this obstacle can be overcome by using a PCM.

Giulia Sandrini, Daniel Chindamo, Marco Gadola, Andrea Candela, Paolo Magri

Open Access

A Propulsion Energy Estimator for Road Vehicles

Residual range estimation plays a crucial role in route selection and the trust of electric vehicles (EVs). With inspiration from longitudinal vehicle dynamics, a simple and computationally efficient model for traction power is presented. Such a model has the advantage of being exclusively based on vehicle exogenous parameters. The model allows for insight into variations in power usage along a transport operation and separation of power losses originating from air drag, rolling resistance, hill climbing, and inertial forces. A model of this structure can handle regenerative braking and estimate service brake usage as an additional feature. Also, it treats the inherent truncation bias resulting from truncating a stochastic process. Evaluation of the performance is presented using Monte Carlo simulations, comparing the estimation error against a simple benchmark model and vehicle log data.

Carl Emvin, Fredrik Bruzelius, Luigi Romano, Bengt Jacobson, Pär Johannesson, Rickard Andersson

Open Access

Black Ice Detection Based on Tire Friction Coefficient Estimation of Vehicle Longitudinal Model

Black ice is a deadly hazard on the road because it is visually transparent and difficult to identify by driver’s naked eye while driving. Because tire friction on a black icy road surface is obviously smaller than normal road, the braking distance significantly increases and leads to severe traffic accidents. Road hazard detection such as black ice has been actively attempted so far, usually focusing on methodology using intelligent vision systems (e. g., cameras). However, current image-based methods are prone to reduced low accuracy due to their susceptibility to vibrations transmitted from road surfaces to vehicles. In addition, incorporating cameras and light detection and ranging sensors increases the complexity and computational burden, especially when extending their functionality to include road surface classification. Therefore, we investigate the potential of new road surface classification based on vehicle longitudinal velocity and tire effective radius estimation from vehicle longitudinal model. This study explores a sensor-fusion type indirect road surface classification algorithm based on Kalman filtering.

Seung-Yong Lee, Ho-Jong Lee, Gi-Woo Kim

Open Access

Experimental Evaluation on Effects of Torque Vectoring Using Four In-Wheel Motor Independent Torque Control

This paper explores the effects of Direct Yaw Control (DYC) on driver’s maneuverability feeling and vehicle dynamics. DYC is implemented in the experimental vehicle equipped with four-wheel independent controlled In-Wheel Motors (IWMs). The effects on vehicle dynamics are validated through open-loop testing. Subsequently, the closed-loop test is conducted to confirm the correlation between subjective evaluation and Vehicle dynamics data.

Yota Homma, Yoshihiro Yamakaji, Koji Kajitani, Richard Ford, Yoshimi Furukawa

Open Access

A Comprehensive Method for Computing Suspension Elasto-kinematics With Non-linear Compliance

Since flexible bushings are used as the interface between the suspension arms and the chassis, the extra degrees of freedom make the design process a complex task. While the use of a multi-body model is common practice in the industry, a dedicated computational tool can be more practical and straightforward, especially when undertaking the design of a new suspension concept from the ground up. This paper presents a quasi-static method for calculating suspension compliance under the action of forces and moments, enabling real-time simulations. The algorithm proposed in this paper was devised with a threefold purpose: integrating elasto-kinematics into the kinematic design tool previously created by the authors, integrating real-time vehicle dynamics simulation, and overcoming the limitations of the traditional approach based on the superposition principle. Finally, a comparison of the proposed model with one based on the lookup-table and superposition principle is presented.

Paolo Magri, Marco Gadola, Daniel Chindamo, Giulia Sandrini, Andrea Candela

Open Access

A Novel Torque Vectoring Approach to Enhance Driving Experience

The current state of Direct Yaw Control literature has reached an advanced level, yielding compelling results in stabilizing vehicle behavior, and enhancing overall performance. However, conventional approaches exhibit limitations in addressing highly transient lateral dynamics, particularly during conditions of rapidly increasing vehicle sideslip angles, leading to poor controllability and reduced intuition for the driver. This paper introduces a novel extension to the established yaw rate tracking technique, integrating the trivial yaw rate feedback control with a less conventional model-based feedforward term, complemented by an innovative sideslip rate tracking loop. The primary objective of this term is to smoothen vehicle cornering response, effectively dampening oscillations in the vehicle’s behavior without compromising time delays to driver inputs. The intended outcome is not only enhancing safety but also delivering a more intuitive and enjoyable driving experience. The effectiveness of the approach is demonstrated by the results obtained through detailed Driver-in-Motion (DiM) sessions.

Marco Paparone, Alessandro Pino, Filippo Giacomel, Giovanni Bussalai, Andrea Macaluso, Antoine Lamps, Basileios Mavroudakis

Open Access

Iterative Learning Trajectory Tracking Control of an Autonomous Bicycle

An autonomous bicycle has been developed for repeatable active safety tests of Advanced Driver Assistance Systems (ADAS). For effective interaction with other test objects, precise bicycle trajectory tracking control is essential. The repetitive nature of these tests suggest an Iterative Learning Control (ILC) approach.In this paper, we present a design of an ILC controller tailored for the trajectory tracking problem of an autonomous bicycle. To illustrate the performance of the controller, simulations have been conducted.

Yixiao Wang, Fredrik Bruzelius, Jonas Sjöberg

Open Access

A General 3D Road Model for Motorcycle Racing

We present a novel control-oriented motorcycle model for nonplanar racetracks and use it for computing racing lines. The proposed model combines recent advances in nonplanar road models with the dynamics of motorcycles. Our approach considers the additional camber degree of freedom of the motorcycle body with a simplified model of the rider and front steering fork bodies. We demonstrate the effectiveness of our model by computing minimum-time racing trajectories on a nonplanar racetrack.

Thomas Fork, Francesco Borrelli

Open Access

Energy-Efficient Straight-Line Driving Torque Vectoring for Electric Vehicles with Disconnect Clutches and Unequal Front/Rear Motors

This paper investigates potential of energy efficiency improvement for electric vehicle (EV) equipped with unequal front/rear-axle e-motors and disconnect clutches under straight-line driving conditions. First, a static optimization of front/rear torque distribution is performed for various driving cycles, which provides insights into energy efficiency gains and optimal powertrain operation including optimal torque switching curve for two- and four-wheel drive modes. Disconnect clutches enable inactive motors to be switched off when operating in the 2WD mode to avoid their drag losses. A dynamic programming (DP)-based optimization of torque vectoring control trajectories is carried out to find the globally optimal energy saving potential. For clutch durability reasons, the number of clutch state changes is minimized along with energy consumption. Finally, a rule-based (RB) control strategy is proposed and verified against the DP Pareto optimal frontier benchmark for different certification driving cycles.

Ivo Grđan, Branimir Škugor, Joško Deur

Open Access

Safety Filter for Lane-Keeping Control

A safe lane-keeping controller is designed using a control barrier function (CBF) which ensures that if the vehicle starts between the lane boundaries then it does not leave the lane. The safety filter is applied on the top of a nominal path-following controller of the kinematic single-track model in order to modify the control input when the vehicle gets close to the boundary of the safe set in state space. Numerical simulations and phase portraits are used to demonstrate the performance of the proposed safety-critical controller.

Chenhuan Jiang, Hanyu Gan, Illés Vörös, Dénes Takács, Gábor Orosz

Open Access

Neural Network Tire Force Modeling for Automated Drifting

Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network’s ability to express previously unmodeled, latent dynamics in the drifting condition.

Nicholas Drake Broadbent, Trey Weber, Daiki Mori, J. Christian Gerdes

Open Access

Basic Study on the Effect of Driver Condition on Steering Burden

With the widespread use of steer-by-wire in automobiles, research is being conducted on high steering gear ratio systems that reduce the burden on the driver, which enables steering with high sensitivity. However, this decreases the resolution during steering and worsens operability, making steering operation more difficult. In addition, if the amount of steering operation and the magnitude of steering reaction torque are not appropriate for the driver, it may cause operation errors. In a highly sensitive steering system, a small change in steering reaction torque has a large potential to affect the driver. The driver’s steering burden varies depending on conditions, so it is not always possible to reduce the steering burden. Therefore, we evaluated the driver’s steering burden on different days and examined the effect of steering reaction force torque on the steering burden. The results confirmed that the deviation of the burden increases as the load increases.

Daigo Uchino, Ri Kintou, Ikkei Kobayashi, Jumpei Kuroda, Kazuki Ogawa, Keigo Ikeda, Taro Kato, Ayato Endo, Hideaki Kato, Takayoshi Narita

Open Access

YOLOgraphy: Image Processing Based Vehicle Position Recognition

A methodology is developed to extract vehicle kinematic information from roadside cameras at an intersection using deep learning. The ground truth data of top view bounding boxes are collected with the help of unmanned aerial vehicles (UAVs). These top view bounding boxes containing vehicle position, size, and orientation information, are converted to the roadside view bounding boxes using homography transformation. The ground truth data and the roadside view images are used to train a modified YOLOv5 neural network, and thus, to learn the homography transformation matrix. The output of the neural network is the vehicle kinematic information, and it can be visualized in both the top view and the roadside view. In our algorithm, the top view images are only used in training, and once the neural network is trained, only the roadside cameras are needed to extract the kinematic information.

Ákos T. Köpeczi-Bócz, Tian Mi, Gábor Orosz, Dénes Takács

Open Access

Surrogate Modeling of Suspensions with High Stiffness Element for Real-Time Analysis Using Machine Learning

The aim of this study is to generate a surrogate model of a suspension system with high stiffness elements for real-time analysis using machine learning. A Long Short-Term Memory networks was used as a machine learning method to generate surrogate models for three-degree-of-freedom quarter car model with a bush element. To evaluate the performance of the machine learning models, the simulation results and computation time were compared with the 3DOF model. As a result, it was confirmed that the response of the body acceleration was predicted with good accuracy by predicting the bush deformation and calculating the body acceleration based on the prediction results, instead of predicting the body acceleration directly. It was also confirmed that the machine learning model can accurately predict the body acceleration in less calculation time than the original model.

Tojuro Hiraga, Taichi Shiiba

Open Access

Data-Driven Risk Assessment with Scenario Classification for Collision Avoidance in Left/Right Turn Across Path Conflicts

Traffic safety is one of the vital issues in developing autonomous and assisted driving systems. To achieve higher level of driving automation, it becomes necessary to have a reliable Risk Assessment (RA) method that not only responds to current traffic conditions but also anticipates future scenario propagation. Regardless of the driver intentions, traditional deterministic risk indicators like Time-To-Collision (TTC) have proven effective but fall short in addressing the inherent uncertainty in future propagation, especially under conflict scenarios influenced by interdependent decisions of traffic participants. Acceleration for Collision Avoidance (ACA) emerges as a flexible risk criterion adaptable to different collision-avoidance maneuvers. Focusing on the Left/Right Turn across path conflicts, this work aims to propose an innovative surrogate risk indicator for collision avoidance with the combination of ACA criteria and scenario classification using Hidden Markov Models (HMM). Based on a near-miss video database, we further train and evaluate the presented model, supplying interpretability and adaptability of risk assessment in complex conflict scenarios.

Wei Wang, Pongsathorn Raksincharoensak, Roman Henze

Open Access

Design and Analysis of Traction Control Strategies for Icy Road Conditions

Traction control (TC) plays a key role in improving vehicle safety, especially for driving scenarios involving extremely low levels of tyre-road friction. In this paper a novel deep reinforcement learning (DRL) based TC strategy is formulated and its performance is compared against a nonlinear model predictive control (NMPC) solution for a simulated straight-line acceleration manoeuvre on icy road conditions. The paper explores the design and assessment of the proposed controllers using a vehicle model experimentally validated on ice. The simulation results show that the DRL solution outperforms the NMPC strategy by reducing the wheel slip ratio peaks and oscillations at the start of an acceleration manoeuvre. Additionally, it converges more quickly to the reference slip and is more computationally efficient.

M. Mihalkov, C. Caponio, Z. Hankovszki, A. Sorniotti, U. Montanaro, P. Gruber

Open Access

Design and Verification of an Adaptive State-Tuned Power Management System for Series Hybrid Electric Tracked Vehicles

The accelerated shift towards electrification in the tracked vehicle industry, particularly concerning off-road and military vehicles, poses challenges due to their intensive power consumption and limited charging infrastructures. Addressing these challenges, this paper focuses on the development of an adaptive state-tuned power management system for a series hybrid electric tracked vehicle. The vehicle's architecture includes an electric traction unit and a hybrid powerpack. The core of this research involves designing a dynamic power allocation system that adjusts the power sharing between the battery and a generator set under varying operational conditions. Through a systematic approach, a power management algorithm featuring a hierarchical controller structure to manage the power flow is designed. Simulation tests, both Driver-in-the-Loop (DIL) and Model-in-the-Loop (MIL), were employed to verify the system’s performance. Results indicate that the algorithm coordinates power distribution, ensuring optimal performance while adhering to the system's constraints and adapting to immediate power demands, demonstrating its potential for enhancing hybrid vehicle operations in demanding maneuvers.

Dersu Çeliksöz, İsmail Göçer, Kerim Arda Gülseren

Open Access

A Study on the Control of Handling and Stability of a Four Wheel Independent Steering Electric Vehicle

The path-tracking control of electric vehicles with four wheel independent steering (FWIS) is crucial for enhancing vehicle stability. This paper aims to address the issue of multi-actuator redundancy by coordinating the control allocation of multiple actuators, thereby improving the handling stability and path tracking performance of FWIS vehicles. First, a model of FWIS electric vehicle is developed, taking into account both the nonlinear tyre and motor actuator characteristics. Subsequently, a path tracking model is established and a hierarchical control architecture is designed. The upper-level controller computes the generalized tracking force and the lower-level control force is distributed based on the principle of optimal tyre utilization rate. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme in terms of tracking accuracy and handling and stability.

Zhihao Wu, Ning Zhang, Pu Li, Zihong Li, Jianrun Zhang

Open Access

Using a Smartwatch to Evaluate Subjective Ratings of Driving Functions

Automating the driving task fundamentally changes the user's driving experience. The driving function now dictates the driving style, previously a product of individual decisions by the driver. Consequently, understanding what constitutes a comfortable driving style becomes essential for ensuring the optimal design of driving functions. This knowledge can be acquired through user studies. As self-reports are often distorted, physiological data can help provide a more objective insight into the emotions and feelings of the test subjects. Smartwatches serve as a convenient and uncomplicated measuring device in this context. The aim of this paper is to investigate whether the accuracy of a smartwatch is sufficient to infer user perceptions and subjective ratings. To achieve this, a subject study is conducted using a driving simulator.

Panzer Anna, Lausch Hendryk, Iatropoulos Jannes, Henze Roman

Open Access

Speed Profile Generation for a Dual Motor Equipped Electrified Series Hybrid Tracked Vehicle Through Dynamic Programming Based Energy Optimization Procedure

Electrification is a widely explored area in many fields and is becoming a widely researched topic in tracked vehicles. Achieving efficient operation for such vehicles is crucial similar to traditional vehicles. This paper introduces a speed profile generation procedure for a dual track – series hybrid tracked vehicle with an emphasis on fuel economy and trip time optimization. A Dynamic Programming method is proposed in spatial domain, considering the properties of predefined road geometries. The study includes examination of speed and torque estimation of the sprockets, particularly during steering maneuvers. Unlike wheeled vehicles, tracked vehicles have to overcome the turning resistance moment of skid steered tracks. Taking these dynamics into account, the study investigates efficient operation, presenting over a predefined road track including straight road, inclined road and curve sections, discussing the tradeoff between fuel economy and trip time optimality. As the result of problem solution, speed profiles are generated, and operation trends of the powertrain components are discussed.

Ismail Gocer, S. Caglar Baslamisli

Open Access

Energy and Time Optimal Control of Autonomous Vehicles by Using Frenet Frame Modelling and over-Actuation

Autonomous driving and electrification make over-actuation technologies more feasible and advantageous. Integrating autonomous driving with over-actuation allows for the effective use of their respective strengths, e.g., for studying energy and time optimal control. To model AVs, several vehicle coordinate systems have been used, e.g., Cartesian, Frenet and spatial coordinates. The present study aims to achieve energy and time optimal control of autonomous vehicles by using Frenet frame modelling and over-actuation. This study enhances the existing Frenet-based modeling by incorporating double-track dynamic vehicle models and torque vectoring. The problem is formulated in an optimal control framework, with carefully designed cost function terms and constraints. Two control strategies are examined, one for minimising travel time and the other for jointly optimising energy consumption and travel time. The results indicate that by considering both energy and time in the formulation, the energy consumption can be apparently reduced while the travel time is merely slightly increased.

Wenliang Zhang, Lars Drugge, Mikael Nybacka, Jenny Jerrelind, Derong Yang, Rudolf Reiter, Jonathan Frey, Annika Stensson Trigell

Open Access

Robust 3D On-Road Object Detection and Distance Estimation for Active Vehicle Control Systems Based on Monocular Camera Image Data

3D object detection from monocular camera videos constitutes a critical research domain. Achieving robust 3D object detection in databases lacking annotated information poses a highly challenging task. This paper proposes a simple yet effective transfer learning approach, integrating data alignment, 3D object detection, and dynamic result correction. Vanishing point detection is employed to infer camera angles in diverse scenes, and preprocessing of new data is conducted by considering the camera's pitch angle and vanishing point position. Subsequently, MonoDETR are applied for depth estimation and 3D object detection in monocular videos. Building upon the detection results, dynamic correction is achieved through inter-frame assistance, culminating in the final 3D object information. Validation on the TUAT Near-Miss Incident Database demonstrates the efficacy of the proposed approach. The results indicate a substantial reduction in the cost of annotating new domain data while simultaneously enhancing detection accuracy and robustness. Integration with onboard diagnostics (OBD) data allows the reconstruction of information about various traffic participants in hazardous scenarios, providing valuable insights for in-depth analysis of accident causes.

Xingguo Zhang, Daiki Ikami, Pongsathorn Raksincharoensak

Open Access

Occluded Area Detection Based on Sensor Fusion and Panoptic Segmentation

Detecting occluded areas in a driving environment is crucial to preventing traffic accidents against hidden road agents coming out from such occluded areas. Our previous work proposed a novel detection method that can offer geometric information of the detected areas by utilizing camera and LiDAR sensor fusion. However, it had difficulty identifying individual areas formed by different objects without information about distinct objects. Thus, the objective of this study was to improve our previous methodology, and panoptic segmentation, which can distinguish between individual objects and offer semantic class labels of the object, was adopted to overcome the limitation. Evaluation results revealed that our proposed methodology could achieve satisfactory results in occlusion area detection and superior accuracy in estimating hidden road agent types in the detected areas.

Hiroshi Yoshitake, Jinyu Gu, Motoki Shino

Open Access

Temporal and Frequency Analysis with Empirical Mode Decomposition for Vehicle Vibration Signals

This study presents the empirical mode decomposition method (EMD) for vehicle vibration and a correlation detection approach for input from road and vehicle body vibration using the Hilbert-Huang transform (HHT). Although the magnitude squared coherence is commonly used to examine the correlation of vehicle vibration with road input, it is not suitable for non-stationary vibration. On the other hand, the Hilbert-Huang transform (HHT) which consists of EMD and the Hilbert transform is proposed. This method is suitable for transient vibration analysis, while the drawbacks to intermittence signals are suggested. Vehicle vibration signals include intermittence signals in some cases. In this study, the masking EMD which adapts the mask signal to the amplitude of the target signal was applied to vehicle vibration to alleviate the drawbacks. By this method, the correlation analysis by HHT demonstrates higher temporal resolution compared to the continuous wavelet transform. Thus, it becomes possible to identify the components that caused vibrations at the moments when the passengers felt comfortable or uncomfortable, and to analyze the characteristics of the vibrations.

Makoto Masuda, Taichi Shiiba

Open Access

Dynamical Behaviours of the Nose Landing Gear with Freeplay and Stochastic Disturbance

Shimmy dynamics of a dual wheel nose landing gear system with torsional freeplay under stochastic lateral disturbances is studied. Dynamic characteristics of the deterministic case are numerically analyzed, especially the shimmy of the landing gear through bifurcation analysis. Meanwhile, the influences of the freeplay nonlinearity on shimmy behaviours are examined in detail. Impacts of stochastic lateral disturbances on the shimmy of the landing gear system are performed via time history and recurrence plots. Our results show that the interaction between the freeplay nonlinearity and the random load induces a significant reduction in the critical shimmy velocity, which has an adverse impact on the stability of the nose landing gear of an aircraft.

Xiaolei Du, Yong Xu

Open Access

Tuning Time Delays to Improve the Performance of a Steering Controller

Time delays and lags in control loops can cause instability and pose significant challenges to engineers. This study investigates a steering controller using the dynamic bicycle model, where the steering system dynamics are approximated by a steering lag. A higher-level controller calculates the desired steering angle based on the vehicle’s lateral position and yaw angle by considering various time delays related to these states. Stability charts are plotted for delay combinations, and the most stable gain setups for the feedback controller are determined. The results indicate that an appropriate increase in one of the time delays expands the stable domain of control gains for the vehicle system, and it enhances the performance of the vehicle controller.

Jialin Li, Denes Takacs, Jianwei Lu, Illes Voros, Gabor Stepan

Open Access

Vehicle Teleoperation: SRPT Approach Resilience to State Estimation Errors Through Simulation Insights

Vehicle teleoperation holds great promise but faces challenges in complex scenarios, limited awareness, and network delays, impacting human operators’ cognitive workload. Our prior work introduced the Successive Reference Pose Tracking (SRPT) approach, transmitting poses instead of steering commands, potentially mitigating delays. Yet, SRPT’s robustness in the face of state estimation inaccuracies and the necessary sensors remain unclear. In this study, we assess SRPT under various challenging environmental conditions and measurement errors using a Simulink-based 14-DOF vehicle model. Results show SRPT’s consistent performance, using estimated states, in worst-case scenarios. Our minimalist sensor setup - IMU, wheel speed encoder, and steer encoder - underscores SRPT’s resilience without relying on GPS, vital for urban environments. This paper highlights SRPT’s robust teleoperation, setting the stage for future real-world vehicle tests prone to measurement errors.

Jai Prakash, Michele Vignati, Edoardo Sabbioni

Open Access

Advancing Autonomous Driving Safety Through LLM Enhanced Trajectory Prediction

In recent years, there has been remarkable progress in autonomous driving technology. To improve the safety of autonomous driving comprehensively, accurate predictions for all traffic agents are crucial. Typically, the graph neural network is widely employed for the trajectory prediction. To enhance the prediction accuracy rate, this paper utilizes a finetuned vision-to-language large model to extract driving intentions. With the well-designed prompt and the supervision of the specific dataset, the LLM (large language model) can analyze the current traffic condition and give the corresponding driving intention. This paper also combines the result of the LLM and the output of the traditional prediction model, and the future trajectory is modified with the driving intention, which can improve the final prediction accuracy. Finally, in the decision-making part, both the driving intention from the LLM and the trajectory from the traditional prediction model are considered in the boundary-based drivable area, and a safe planning path is then generated. According to the validation in the public motion forecasting dataset, this method has greatly improved the accuracy of the prediction and the safety of route planning.

Qian Cheng, Xinyu Jiao, Mengmeng Yang, Mingliang Yang, Kun Jiang, Diange Yang

Open Access

Self-tuning of the Virtual-Bike Control for a Human-Powered Electric Bike with Series Architecture

Motivated by environmental awareness, electric bikes (e-Bikes) diffusion as a means of transport has significantly increased in cities, thanks to their low emission and footprint. Among the different alternatives, series-parallel e-Bike architectures are interesting because they merge the advantages of the most common parallel bikes and the series ones, which can be exploited to improve the user experience. When an e-Bike is operating in series mode, a specific control action is needed to handle the absence of a mechanical transmission and so the chain-less nature of series or series-parallel e-Bikes. To this aim, the virtual-chain control law has been proposed and recently extended to a virtual-bike approach, respectively aiming at emulating the experience of the chain or an entire bike, whose parameters are user-chosen. In this work, a self-tuning strategy for the control parameters in the virtual-bike approach is formulated, making it independent of the specific bike and rider. Experimental results showed the advantages and limitations of the proposed solution.

Stefano Radrizzani, Giulio Panzani, Sergio Matteo Savaresi

Open Access

Cooperative LiDAR-Aided Self-localization of CAVs in Real Urban Scenario

In the rapidly advancing realm of Connected Autonomous Vehicles (CAVs), achieving reliable and precise positioning is of paramount importance. This paper presents a comprehensive approach integrating vehicular sensing, communication, and advanced filtering techniques to enhance vehicle positioning in urban areas. By leveraging LiDAR point clouds along with a light and accurate object detector, we create cohesive environmental sensing that improves situational awareness in autonomous systems. Central to our methodology is the integration of the Labeled Multi-Bernoulli Mixture (LMBM) filter, which offers a probabilistic framework for dynamic state estimation in environments characterized by high uncertainty and clutter. In turn, enhanced object locations are exploited as anchors for vehicular self-localization via an Extended Kalman filter (EKF). Our experimental results show that the proposed solution significantly enhances vehicular positioning accuracy.

Akif Adas, Luca Barbieri, Pietro Morri, Simone Mentasti, Satyesh Awasthi, Stefano Arrigoni, Edoardo Sabbioni, Monica Nicoli

Open Access

Subjective-Objective Assessment of Different Torque Vectoring Control Strategies

With the increasing demand for enhanced vehicle performance and handling dynamics, torque vectoring has become a key technology in contemporary automotive engineering. Over the years, various control algorithms have been developed and their performance assessed using objective metrics that measure a vehicle's ability to follow a reference quantity for lateral dynamics. However, this evaluation approach is limited as it does not consider feedback from actual drivers. This paper addresses this critical gap by incorporating drivers’ subjective evaluations through Driver-in-the-Loop simulations. The study conducts a comprehensive analysis of both objective metrics and subjective driver assessments, investigating the impact of different torque vectoring control strategies on vehicle dynamics. By correlating quantitative metrics with human perception, this integrated approach enhances the understanding of the effectiveness of torque vectoring control strategies and their real-world implications for driver satisfaction.

Michele Asperti, Michele Vignati, Edoardo Sabbioni

Open Access

Retaining Cornering Performance and Reducing Energy Consumption with Torque Vectoring and Suspensions Tuning

With the automotive industry's shift towards sustainability and energy efficiency, optimizing vehicle handling dynamics has become secondary. Additionally, there is a growing trend towards comfort-oriented design over handling performance. However, advancements such as integrating multiple independently controlled electric motors enable torque vectoring, offering a promising solution for reconciling these conflicting objectives. This paper proposes a novel approach to jointly improve vehicle handling and energy efficiency. Advanced simulation techniques are used to explore various suspension configurations to balance cornering performance and energy consumption. A torque vectoring controller is then designed in combination with meticulously tuned suspensions. This innovative approach, which considers active control design alongside suspension setup, achieves superior performance. Desired vehicle cornering capabilities are attained while ensuring significant efficiency in straight-line driving, which constitutes most road driving.

Michele Asperti, Michele Vignati, Edoardo Sabbioni

Open Access

Enhancing Steer-by-Wire Systems with an Integrated E-Motor and MR-Brake Actuator – Feedback Control Strategy

This study introduces an innovative control strategy for a steer-by-wire (SbW) force feedback actuator, tailored for automotive use. The actuator integrates a direct drive electric motor and a magnetorheological brake (MR-brake) within a streamlined, compact design. This electric motor delivers torque essential for the desired steering feel and ensures swift responsiveness. Concurrently, the MR-brake, noted for its compactness and energy efficiency, contributes passive damping and robust end-stop torque capabilities. The synergistic use of these components allows for a smaller and more resource-efficient electric motor design. A critical aspect of this strategy is the implementation of a torque splitter, essential for replicating the steering feel associated with Electric Power Steering (EPS) systems. This splitter distributes the required feedback torque between MR-brake torque and e-motor torque, ensuring high fidelity and consistent stability across a range of vehicular dynamics and maneuvers. Notably, maintaining a subjectively satisfying steering feel under conditions where torque proportions vary between actuators presents a significant challenge. The proposed actuator design, with its focus on performance, safety, and reliability, is particularly well-suited for advanced SbW systems demanding high standards in these areas.

Matthias Niegl, Johannes Hendewerk, Matthias Becker, Stefan Battlogg, Peter Pfeffer

Open Access

A Rule-Defined Adaptive MPC Based Motion Planner for Autonomous Driving Applications

In autonomous driving systems, motion planning to reach a given destination while avoiding obstacles becomes a task entirely managed by the on-board unit. In this work, we present a rule-defined motion planning algorithm for autonomous driving applications based on an adaptive Model Predictive Controller (MPC) framework. The motion planning task is first formulated as an Optimal Control Problem (OCP) subject to time-varying Control Barrier Function (CBF) constraints. It is then integrated within an MPC framework with adaptive weights settings, enabling the algorithm to dynamically adjust the MPC weights according to the rule-defined driving scenarios. The developed motion planner generates optimized trajectories for a high-fidelity Autonomous Vehicle (AV) model within IPG CarMaker software. Simulations performed showed that the developed motion planner adeptly facilitates successful overtaking, following, and stopping of the AV behind the Obstacle Vehicle (OV) based on rule-defined scenarios perceived by the AV.

Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Satyesh Shanker Awasthi, Michael Khayyat, Stefano Arrigoni, Francesco Braghin

Open Access

Towards Friction Potential Estimation for Motorcycles

Estimation of the actual friction potential from measured vehicle states has been extensively explored for passenger cars, but lacks attention for motorcycles. Several aspects towards friction potential estimation for motorcycles are discussed in this study: (i) analysis of the characteristics of motorcycle tyres on different surfaces and conditions by using an instrumented motorcycle, (ii) analysis of the theoretical performance of an EKF-based friction potential estimation approach by means of simulation studies, and (iii) analysis of the feasibility of using a wetness sensor on the motorcycle to gain information on actual road conditions.

Florian Klinger, Christoph Ott, Agnes Poks, Johannes Edelmann, Manfred Plöchl

Open Access

On the Stability of the Closed-Loop Teleoperated Vehicle and Teledriver System

Teleoperated vehicles are gaining importance during the transition to fully automated vehicles since at the current state of development automated vehicles are not capable of operating in all conditions and environments. Despite the high potential of the concept of teleoperated vehicles, there are still challenges to be dealt with. As the teledriver is physically not present in the vehicle during teleoperation, the communication between the teledriver and the vehicle takes place via internet. That could lead to a delayed transmission of the telemetry and visual signals. Additionally, the teledriver’s perception of the vehicle motion is reduced. This study demonstrates the differences between teledriving and normal driving from a theoretical perspective and how the stability of the vehicle-teledriver system is affected by the communication delay and the reduced perception of the vehicle motion. To increase the driver’s perception and the ease of control of the teleoperated vehicle, a simple structured and tunable steering wheel torque emulation concept is introduced.

Ypti Hossain, Mathias Metzler, Johannes Edelmann, Manfred Plöchl

Open Access

Increasing Performance of Differential Braking as Steering Backup Using Combined Slip Effects

Redundancies provide essential fail-operationality and are commonly utilized in design of Steer-by-Wire systems. Operationality of steering rack actuators can therefore be increased using differential braking as additional redundancy level. Research in this field has shown boundaries for lateral control to be mitigated compared to conventional steering systems. This paper focuses on performance increases by driveline torque and stability control, both yielding effects of combined longitudinal and lateral tire slip during cornering.Coping with the tradeoff between under- and oversteer whilst increasing performance was the major aim during controller design. For vehicle prototype testing a model following control scheme for differential braking was derived and implemented. Measurements on a low friction proving ground in suitable driving maneuvers were carried out subsequently. The results show new boundaries of vehicle dynamics and address variations of steering kinematics parameters as well as driveline configurations. Investigated interactions of longitudinal and lateral tire slip in the field of differential braking brings up promising potential to increase cornering ability in the fallback level for steering rack actuators.

Leon Salzwedel, Christian Frohn, Cedric Heise, Jannes Iatropoulos, Roman Henze

Open Access

Laboratory Abrasion Tester to Estimate Tyre Grip and Cornering Stiffness

Identification of tyre grip is crucial for ensuring safety and performance. To develop new tyres matching the requirements for grip, extensive testing is required both indoor and outdoor, which is time-consuming and costly. Therefore, the possibility to assess their performance in laboratory before manufacturing a full tyre appears very attractive. On this purpose, the present paper compares the peak of the friction coefficient, evaluated using a Laboratory Abrasion Tester (LAT100) on two compounds and the grip assessed through MTS Flat-Trac machine tests on two full tyres having the same structure and made of the compounds tested on the LAT100. An ad hoc procedure for driving the test on the LAT100 and make them comparable with the full-tyre data was developed. A good correlation was found for the dependency of the friction coefficient on temperature and load, highlighting the possibility of using LAT100 tests to gain information about tyre performance, before the manufacturing of the full tyre.

Francesco Colombo, Samuel Sonnino, Federico Mantovani, Andrea Ronchi, Luca Michielan, Michele Vignati, Edoardo Sabbioni

Open Access

Indoor Tyre Tread Wear Testing Driven by Outdoor Data Clustering

Wear is becoming a topic of major attention for tyres, affecting also other performances. Therefore, its estimation is of utter importance under several points of view, such as predictive maintenance and vehicle dynamics controllers. Indoor testing is emerging as an alternative way for predicting wear compared to on-road outdoor tests, which nowadays represent the standard methodology. Indoor tests, in fact, are performed in a more controllable environment, reducing testing time and costs. However, several challenges must be faced to reproduce indoor the same wear rate/shape obtained in real on-road working conditions. The present paper focuses one of the critical aspects for indoor testing: the definition of the load cycle to be applied to a tyre, i.e. the time history of forces, slip and angles to be provided as an input to the wear machine. Specifically, a clustering approach able to extract from outdoor data a limited set of manoeuvres representative of a given outdoor wear track is proposed.

Lorenzo Maglia, Davide Fantin, Stefano Pontoglio, Matteo Stella, Edoardo Sabbioni

Open Access

Trailer Reversing Supported by Steer-by-Wire

Backing up a trailer can be a daunting task, even for experienced drivers. The main challenge being the unstable property of car-trailer kinematics when reversing. With steer-by-wire systems, the mechanical connection between the steering wheel and the road wheels is replaced by an electrical connection. This means that road wheels no longer have to be directly connected to the steering wheel input. The aim of this paper is to help the driver to steer the trailer directly by stabilising the car-trailer kinematics during reversal. This is achieved by developing a steer-by-wire system coupled with a closed-loop trailer reversal control using the necessary feedback. How to obtain this feedback is further investigated in this paper as well as how to use the steering wheel input and torque feedback to interact with the backup assist function. The developed control and user interaction is subjectively and objectively evaluated using computer simulation and a physical prototype of a vehicle equipped with steer-by-wire.The results from the simulation experiments demonstrate that drivers with and without experience of driving a trailer can do the wanted manoeuvres with higher accuracy as well as within a short time span with the controller that is developed in this work. The results from the real life experiments also appears to indicate that the system can remove stress from the driver and move the trailer in an accurate way during a parking manoeuvre.

Chang Liu, Jakob Roempke, Matthijs Klomp, Lars Drugge

Open Access

A Yaw Rate Based Stability Control Tuning via Virtual Methods

Electronic Stability Control (ESC) allows to prevent safety-critical scenarios and vehicle sideslip angle estimation plays a crucial role in such applications. Time to market and safety concerns in the development and validation of such algorithms are leading the automotive industry towards virtual methods. The recent introduction of driving simulator technologies on vehicle development process allows to develop calibrate and test in advance virtual prototypes of full vehicle and its controls obtaining objective and subjective evaluation during the early stage of the vehicle development. This paper presents the methodology and results related to the development and calibration of a yaw rate based stability control grounded on a mixed-kinematic sideslip estimator. This work has been carried out leveraging tools provided by simulation platforms, scalable configurations of driving simulators and results from road tests.

Luca D’Avico, Fabio Carbone, Lucas Baudry, Fabrizio Forni, Pietro Caresia, Gerardo Amato

Open Access

Optimal Braking and Steering Control Under Split Friction on Curved Roads

This paper aims to maximize deceleration on split friction roads by combining steering and individual wheel braking. For this, a previously tested optimization problem is adapted to curved roads. The optimal brake force and steering allocation is investigated as a function of the split friction asymmetry. Results show that low friction is more detrimental to maximum braking on the inner side of the curve due to load transfer. Finally, the paper showcases a control strategy for braking on split friction, which enhances safety and manoeuvrability in critical split friction scenarios.

Ektor Karyotakis, Derong Yang, Mats Jonasson, Jonas Sjöberg

Open Access

A Fallback Approach for In-Lane Stop on Curved Roads Using Differential Braking

Due to the importance of safety in the development of Automated Driving Systems (ADS), research is ongoing on fallback functions for ADS. Scenarios involving steering actuator failures during curved road driving are particularly dangerous, with limited available fallback options. This paper proposes a method for achieving a safer in-lane stop using differential braking when steering actuator failure occurs. Lateral motion is induced by the difference in longitudinal forces between the left and right sides, facilitating an in-lane stop maneuver. The vehicle’s lateral behavior under differential braking and changes in front-wheel steering angle are modeled. A model-based estimator using a Kalman filter estimates the vehicle’s state and steering angle. Based on these estimations and lane information, a controller employing a linear quadratic regulator (LQR) is developed. The effectiveness of the differential braking in-lane stop system is validated through simulation.

Jihoon Sung, Seungwon Choi, Kunsoo Huh

Open Access

Steering Noise Cancelling for Drift Assist Control

In this paper we consider a driver assist system concept for the stabilization of the vehicle during high sideslip angle cornering (drifting). Unregulated steering inputs (steering noise) from inexperienced drivers, can disturb the drift equilibria and oppose to the controller’s stabilization task. Therefore, this paper presents a drift assist control concept to cancel the steering effects from the driver by torque vectoring. In detail, first by equilibria analysis for four-wheel drive vehicle, the steering effects on drift equilibria are analyzed, which indicates a contradiction between stabilizing the yaw rate and the sideslip angle. Then, by nonlinear programming, it is proven that the path angle rate together with the speed which determine the trajectory, could be stabilized with torque vectoring against the steering noise. Finally, a steering noise cancelling and trajectory tracking controller structure is designed and further evaluated with CarMaker.

Yiwen Sun, Efstathios Velenis, Ajinkya Krishnakumar

Open Access

A Decoupling Control Scheme for Path Tracking with Model Predictive Path Integral and Output Regulator

The coupling and nonlinearity of vehicle dynamics present considerable challenges to path tracking of autonomous vehicles. In this paper, a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently, the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a high-fidelity control model. In addition, model predictive path integral (MPPI) is developed to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. A predictive output regulator is developed to solve the underactuated problem in the 2-DOF lateral dynamics with only 1-DOF of control input. Simulation results show that the proposed method enhances computing efficiency and reduces the lateral jerk by an average of 50% with only one set of parameters.

Hang Wan, Hui Liu, Shida Nie, Lijin Han

Open Access

A Learning-Based Model Predictive Contouring Control for Vehicle Evasive Manoeuvres

This paper presents a novel Learning-based Model Predictive Contouring Control (L-MPCC) algorithm for evasive manoeuvres at the limit of handling. The algorithm uses the Student-t Process (STP) to minimise model mismatches and uncertainties online. The proposed STP captures the mismatches between the prediction model and the measured lateral tyre forces and yaw rate. The mismatches correspond to the posterior means provided to the prediction model to improve its accuracy. Simultaneously, the posterior covariances are propagated to the vehicle lateral velocity and yaw rate along the prediction horizon. The STP posterior covariance directly depends on the variance of observed data, so its variance is more significant when the online measurements differ from the recorded ones in the training set and smaller in the opposite case. Thus, these covariances can be utilised in the L-MPCC’s cost function to minimise the vehicle state uncertainties. In a high-fidelity simulation environment, we demonstrate that the proposed L-MPCC can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre at a higher velocity than an MPCC without STP. Furthermore, the proposed controller yields a significantly lower peak sideslip angle, improving the vehicle’s manoeuvrability compared to an L-MPCC with a Gaussian Process.

Alberto Bertipaglia, Mohsen Alirezaei, Riender Happee, Barys Shyrokau

Open Access

Predictive Braking on a Nonplanar Road

We present an approach for predictive braking of a four-wheeled vehicle on a nonplanar road. Our main contribution is a methodology to consider friction and road contact safety on general smooth road geometry. We use this to develop an active safety system to preemptively reduce vehicle speed for upcoming road geometry, such as off-camber turns. Our system may be used for human-driven or autonomous vehicles and we demonstrate it with a simulated ADAS scenario. We show that loss of control due to driver error on nonplanar roads can be mitigated by our approach.

Thomas Fork, Francesco Camozzi, Xiao-Yu Fu, Francesco Borrelli

Open Access

Variable-Step-Length Hybrid A* Based on Dichotomy Optimization for Path Planning of Autonomous Mining Trucks*

Global path planning for autonomous mining trucks in shovel-loading areas requires path optimality and high computational efficiency. However, generating qualified paths for the loading process is more difficult for conventional methods under accurate final pose constraints. Furthermore, due to large-scale maps with variable obstacle distribution, considerable computation time needs to be allocated for path planning when using conventional methods. To address this problem, a novel Variable-Step-Length Hybrid A* based on Dichotomy Optimization (DO-VSLHA*) algorithm is proposed to generate obstacle-free paths considering mountain morphology and vehicle constraints while reducing computation time. To avoid U-shape obstacles and unnecessary node search, the clustering method is applied to the grid map to generate convex polygon obstacles. Subsequently, with joint sampling of step length and steering angle, we put forward dichotomy optimization based on the cost function in order to generate near-optimal nodes in each loop of the node expansion process, thus reducing the overall computing time of path planning. Field experiments are carried out on autonomous mining trucks at an open-pit mine, validating the improvement in effectiveness and computational efficiency of our method compared to conventional methods.

Yichen Zhang, Yafei Wang, Mingyu Wu, Ruoyao Li

Open Access

Study on Continuous Road Friction Measurement Under Various Environmental Conditions

This paper deals with a study to construct a road friction characteristic database as the first step in building a system to estimate road friction characteristics ahead, which is one of the major issues in traffic safety. To achieve this goal, we proposed a new method that can continuously measure the peak μ in the μ-s characteristic and showed the measurement results on different road surfaces. Based on these results, in this paper we measure the friction characteristics of snow and ice surfaces at a well-maintained test site and present the continuous μ-s characteristics on the surfaces and the peak μ regions on each surface. Furthermore, measurements are also performed on ordinary roads under snowy conditions, and a comparison with the results from the proving ground, it is shown that we need to examine the dynamics of the μ-s characteristics.

Ichiro Kageyama, Atsushi Watanabe, Yukiyo Kuriyagawa, Tetsunori Haraguchi, Tetsuya Kaneko, Minoru Nishio

Open Access

Observer Design for Estimating Road Elevations at All Tire Contact Patches Using Only an Inertial Sensor

This paper designs an observer for estimating road elevations at all tire contact patches using only an inertial sensor with high accuracy, comparable to that of laser scanning. The observer is constructed within the framework of the unknown input Kalman filter to estimate road elevations, which act as disturbances to vehicle dynamics. The model for the observer is based on vertical-pitch-roll dynamics, encompassing road elevations at all tire contact patches. The introduction of virtual measurements for all tires ensures the observability of the model in the inertial sensor-based observer without requiring both additional sensors and model simplification. Additionally, a bias model is added to compensate for sensor installation errors for practical realization. Experimental validation demonstrates that the proposed observer can estimate road elevation with high accuracy, regardless of vehicle speed and dynamics, even when utilizing only an inertial sensor, making it suitable for rapid and robust road maintenance.

Hosik Choi, Juhui Gim

Open Access

Physics-Informed Neural Network for Mining Truck Suspension Parameters Identification

Mining truck suspensions are prone to performance degradation under complex external excitation of the mining area, leading to high safety risks and maintenance costs. However, the lack of unsprung kinematic information and harsh operating environments lead to inadequate accuracy of current physical models. On the other hand, data-driven methods partially address the issue of incomplete information, but suffer from the absence of interpretability and generalization. To address these challenges, this paper introduces a Physics-Informed Neural Network (PINN) for precise suspension characteristic identification of mining trucks. Specifically, the physical model of the longitudinal-vertical dynamics of the mining truck is established. Then, based on the model, baseline values of suspension parameters are regressed through the instrumental variable method. Therefore, the hybrid modeling architecture is established to precisely identify suspension parameters by utilizing a recurrent neural network. Under this architecture, the states of the mining truck can be effectively updated. Real truck experiments demonstrate the proposed hybrid model outperforms traditional physical and data-driven models in estimating suspension nonlinear parameters and truck dynamic characteristics under typical longitudinal motions.

Mingyu Wu, Yafei Wang, Yichen Zhang, Zexing Li

Open Access

Point Cloud Interpolation by RGB Image to Estimate Road Surface Profile for Preview Suspension Control

The growing prevalence of autonomous driving is expected to shift passengers’ attention from driving, increasing the demand for enhanced ride comfort. Studies addressing ride comfort have prominently explored active suspension control with recent research on preview suspension control using on-board sensors. The proposed systems often include LiDAR deployment at the front for high-precision road surface profiles. However, these systems often involve costly sensors such as LiDAR, making it impractical for on-board installation. Nonetheless, in recent autonomous vehicles, LiDAR tend to be mounted on the roof. It would be beneficial to leverage this LiDAR for preview control, the point cloud obtained from the roof has insufficient density to accurately perceive the unevenness on the road surface. To overcome the low-density issue in point cloud data obtained from less channels LiDAR, this study applies a supervised machine learning model, developed for autonomous driving, to estimate road surface profiles and enhance the precision of these estimations.

Masato Inoue, Yosuke Kawasaki, Takuma Suzuki, Yuta Washimi, Tsutomu Tanimoto, Masaki Takahashi

Open Access

Gain-Scheduled Bicycle Balance Controller Based on System Identification

Balancing a bicycle through steering is similar to balancing an inverted pendulum, with a travel-speed-dependent pivot point. This paper derives a speed-dependent balancing controller for a self-balancing bicycle. This controller is based on an identified gray box model. The identification procedure is formulated as a weighted least squares problem with the time-varying parameter of the model. Identification data was generated on a controlled bicycle robot. Excitation experiments were designed to account for the unstable nature of the problem. Based on this identified model, a gain-scheduled controller is derived for a speed-independent closed-loop performance for a speed range. The controller is further implemented on the bicycle and tested for a set of speeds. Tests performed on the bicycle illustrate the gain-scheduled controller’s performance gain.

Yixiao Wang, Fredrik Bruzelius, Jonas Sjöberg

Open Access

Investigating Reversing Motion of Truck-Semitrailer Along Clothoid Curve

The focus of the research is on the most common transport vehicle, the truck-semitrailer combination. A single-track kinematic model is used, supplemented by the dynamics of the steering system, to design a linear state feedback controller for the low-speed path-following problem in reverse. Despite the significant time delay considered in our study, the linear stability of the system is ensured, even for complicated maneuvers. The advantages of the adaptive gain tuning method are presented based on simulations of the commonly used 90-degree alley dock.

Levente Mihályi, Dénes Takács

Open Access

A Way Beyond Drifting: Cornering at the Unexploited Region of Dynamics

Linear range considerations in the tire-road contact restrict the potential of dynamic control designs of passenger vehicles, while strong nonlinear dynamics such as drifting are intrinsically dangerous. In this paper, based on the chassis with independent wheel drive/brake torque control, we propose a model-based strategy to exploit the potentials of all four tires in combined slips to elevate the cornering performance in critical maneuvers. The model-inversed results indicate that the maximal achievable steady-state (SS) yawing is larger than the widely-used boundary for control system design. Nonlinear dynamics around the created stable motions are further analyzed by plotting phase planes. With feedback control incorporated, the proposed strategy is verified in simulations for both local and global dynamics. The method also shows a distinctive availability of tuning different vehicles into desired driving characteristics and elevating their performance levels through independent powertrains.

Hangyu Lu, Xiaodong Wu, Liang Yan

Open Access

Design and Implementation of a Slip Control for Electric Formula Student Vehicle Using Sliding Mode Control

This paper presents a slip control design method for a four wheel driven electric race car with low hardware requirements. In addition, to achieve robustness against the changing frictional conditions, a discrete-time Luenberger tractive force observer is designed. The tuning is carried out using the high-precision vehicle dynamics simulation software CarMaker. The performance of the controller is demonstrated in real-world tests. An extensive comparison is given to show the advantage of the proposed method over a previously designed PID controller.

Ádám Alföldi, Dániel Fényes, Péter Gáspár

Open Access

Long Combination Vehicles Reverse Strategies Based on Articulation Angle Gradient

To guide the development of driver assistant systems and fully automated solutions for reversing long combination vehicles (LCVs), the principles for reversing LCVs are investigated using the articulation angle gradient. The widely used Steady-state Circling Limitation (SSCL) in reversing LCVs has two main drawbacks: it restricts vehicles from operating with large articulation angles crucial for tight spaces and lacks a well-defined feasible range. Two new reverse principles are introduced that can provide better insight. The first principle extends SSCL to include more extreme articulation angles for single-articulated vehicles. It also addresses the necessity of considering articulation gradients when developing the continuous reverse limitation for multi-articulated vehicles. The second principle introduces limited distance reversing for vehicles that no longer meet the first principle’s requirements, providing additional vehicle ending poses useful for tasks like loading and coupling.

Zhaohui Ge, Fredrik Bruzelius, Bengt Jacobson

Open Access

A Comparative Study of Discomfort Using Electrical and Friction Braking at Low Speed Driving

In this study, we conduct an analysis of the longitudinal dynamics of a vehicle model in an incline, with a specific focus on its behavior, at low speeds, when starting and stopping. The model is minimal, yet an effective representation of a vehicle that includes the effects of springs and dampers as well as friction and electric braking models, which allows for easy analysis into their interplay at low speed. One important feature that this early study shows is how the acceleration and jerk is affected by static and dynamic friction coefficients in different driving situations. Our study further demonstrates the interplay between the electric and friction braking systems and the differences in oscillatory motion they generate. Such insights are vital if we want to improve vehicle control at low speeds and suggest ways to reduce problems like excessive acceleration and jerk. Additionally, our findings could also provide valuable insights when developing active friction braking systems.

Samira Deylaghian, Mats Jonasson, Petri T. Piiroinen

Open Access

Deriving Models from Field Test Data to Forecast Brake System Limits in Fuel Cell Heavy-Duty Trucks

Evaluating braking system limits is crucial in designing heavy-duty trucks, often requiring extensive time and resources through field and dynamometer testing. To reduce these demands, modeling approaches have been widely adopted. However, it faces challenges in complex configurations like fuel cell trucks due to interactions between brake and energy systems, particularly regenerative braking, a feature absent in conventional heavy-duty trucks. This paper presents a model that simplifies the representation of these systems in fuel cell trucks, using data-driven models based on field tests. It details constructing and validating a comprehensive brake system model specifically for downhill scenarios in fuel cell trucks, achieving around 99% accuracy in predicting brake limits.

Seongjae Mun, Jinhui Park, Hongwoo Lee, Changsun Ahn

Open Access

Numerical Study on Vibration Characteristics of Non-pneumatic Tire Coupled with Quarter-Car Model

Non-Pneumatic Tires are primarily recognized for their puncture-free attributes, particularly suitable for specialized vehicles. However, not only the advantages, but also vehicle characteristics such as noise, vibration, and harshness need to be considered in case of application to standard passenger cars. To address this, estimating the vibration characteristics of NPT, considering the nonlinear behavior of the tire and its interaction with other car components, is important for vehicle development and chassis control. In this study, tire finite element analysis combined with the multibody simulation of a quarter-car model is employed. The vibration characteristics of a passenger car equipped with NPT are investigated on a specific tire construction and a car model in comparison to a pneumatic tire. It was found that the NPT exhibits high-frequency characteristic vibrations, although the overall trend is qualitatively similar to that of the pneumatic tire when the vertical stiffness and the contact properties are set to be close to those of the pneumatic tire.

Yuta Washimi, Takuma Suzuki, Toshihiko Okano, Kensuke Sasaki

Open Access

Study on the Effects of Long-Term Vibration and Visual Tasks on Visual Acuity in the Car

In this research, to maintain a passenger’s good visual acuity during long riding, we investigated the effects of long-term vibration and visual task on the visual acuity. The results showed that after performing the prolonged visual tasks in the vibration-exposed state, the visual acuity in the stationary state remained unchanged, but the visual acuity in the vibration-exposed state decreased. From these results, it is assumed that among the functions of the eye, the function of stabilizing the eye against vibrations is most likely to deteriorate due to fatigue caused by visual loads. Consequently, to maintain visibility in the car during long car trips, it is important to suppress head movement. This helps prevent fatigue in the eye stabilization function.

Masateru Amano, Aya Kubota, Hiroyuki Yamaguchi, Yuji Muragishi, Yoshikazu Hattori

Open Access

Human-Centered Collaborative Decision-Making and Steering Control with Reinforcement Learning

This paper presents a novel human-centered collaborative driving scheme using model-free reinforcement learning (RL) approach. The human-machine cooperation is achieved in both decision-making and steering control levels to improve driving safety while leaving space for human freedom as much as possible. A Markov decision process is firstly derived from the collaborative driving problem, then a RL agent is developed and trained to cooperatively control the vehicle steering under the guidance of a heuristic reward function. Twin delayed deep deterministic policy gradient (TD3) is conducted to attain the optimal control policy. In addition, two extended algorithms with distinct agent action definitions and training patterns are also devised. The effectiveness of the RL-based copilot system is finally validated in an obstacle avoidance scenario by simulation experiments. Driving performance and training efficiency of different RL agents are measured and compared to demonstrate the superiority of the proposed method.

Liang Yan, Xiaodong Wu, Hangyu Lu

Open Access

A Study on Giant Magnetostrictive Actuator Used in Active Noise Control System for Ultra-compact Electric Vehicles (Analytical Consideration on Output Performance of the Actuator)

The interiors of ultra-compact electric vehicles (EVs) can be uncomfortable owing to the noise caused by the road and wind. To address this issue, we propose an active noise control (ANC) system that uses a giant magnetostrictive actuator. The proposed system allows estimating ride comfort by analyzing the biological information of passengers and controlling the interior acoustic environment. The proposed ANC system employs wall-surface vibrations generated by a giant magnetostrictive actuator. We analytically investigated the thrust characteristics of giant magnetostrictive materials deformed by a magnetic field through electromagnetic field analysis. The results showed that the effect of thrust on frequency changes depends on the characteristics of the giant magnetostrictive material.

Taro Kato, Ryusei Naganuma, Koki Bando, Ikkei Kobayashi, Jumpei Kuroda, Daigo Uchino, Kazuki Ogawa, Keigo Ikeda, Ayato Endo, Xiaojun Liu, Hideaki Kato, Takayoshi Narita, Mitsuaki Furui

Open Access

Collision Prediction for a Mining Collision Avoidance System

Accidents caused by wheeled mining machines contribute to approximately 30% of injuries and fatalities in the global mining industry. Wheeled mining machines have limited driver assist features when compared to the passenger vehicle market and are typically limited to collision avoidance by braking. These products are often subject to false positive interventions leading to production losses, increased wear, and resistance to adopt the technology by end users. This study proposes a sampling-based method to expand the collision avoidance by braking approach to include steering. The sampling method is based on the vehicle’s kinematics and the application of a Gaussian distribution to the steering rate to determine the probability of a collision occurring. Initial results indicate that the inclusion of steering rate on the collision prediction model may increase the operator’s situational awareness, leading to fewer false positives.

J. C. van Aswegen, H. A. Hamersma, P. S. Els

Open Access

Interactive and Robust Prevention of Lane Departure

Lane Keeping Assistance (LKA) is one of the most common Advanced Driver Assistance System (ADAS) functions on the market, yet it is still not well accepted by drivers. Although LKA can reduce the occurrence of traffic accidents by correcting the vehicle heading in the event of an unintentional lane departure, poor usability often results in manual deactivation of the function. We provide considerations on how to specify LKA functions in general and propose admittance control as a solution to improve the state-of-the-art from the widely used concept of torque overlay, which is limited by a trade-off between automated and manual driving modes. Using the proposed LKA function, unintentional lane departure is prevented while maintaining comfortable reaction torque which allows the driver to easily steer the vehicle.

Syouma Edamoto, Shuuji Kimura, Tsutomu Tamura, Richard Gao, Robert Fuchs

Open Access

Effect of Control Laws for Torque-Vectoring Systems on Steady-State Cornering in Race Cars

This paper presents an analysis of the control law for a torque-vectoring system that actively distributes left and right drive torques to maximize the steady-state cornering performance of a rear-wheel drive race car. The control law allocates torque to the left and right vertical load distribution. Steady-state analysis results show that the maximum lateral acceleration could be improved by 7.8% by decreasing the maximum slip ratio and altering the point at which the yaw moment is trimmed compared to the existing passive system. Furthermore, the stability of the system is reduced, and measures to ensure stability are presented.

Ikkei Kobayashi, Fumiya Yoshida, Liting Fu, Yusuke Ebashi, Hayato Yamada, Jumpei Kuroda, Daigo Uchino, Kazuki Ogawa, Keigo Ikeda, Taro Kato, Ayato Endo, Mohamad Heerwan Bin Peeie, Hideaki Kato, Takayoshi Narita

Open Access

Mapping and Localization Method for Autonomous Vehicles on Roads Using Environmental Magnetic Field

Vehicle localization is one of the key technical factors for autonomous vehicles. It requires high accuracy, precision, and robustness towards various road conditions. Popular localization methods include global navigation satellite system (GNSS) and visual methods, but their accuracy can degrade in some conditions. This work proposes to use the environmental magnetic field (EMF) for localization to complement the shortcomings of existing methods. EMF is a combination of the Earth’s geomagnetic field and magnetic field induced by man-made objects. It has local fluctuations that can be paired with coordinate positions and is time-invariant within a practical timescale. Past works considering the localization of road vehicles had few problems when applying them to the localization of autonomous vehicles. This work overcomes the problems in the existing method by creating a two-dimensional magnetic field map using Gaussian Process regression, using magnetic markers to enhance EMF fluctuations, and utilizing the Monte Carlo localization algorithm. The proposed method was validated through actual vehicle tests, and its robustness towards other vehicles was examined.

Kyoya Ishii, Keisuke Shimono, Yoshihiro Suda, Takayuki Ando, Hirotaka Mukumoto, Kazuo Urakawa

Open Access

Consideration of Restoration in Yaw Resonance

Yaw resonances cause the rear-end of the vehicle to swing, which is related to the feeling of handling. As a basis for improving this motion, this paper considers the restoration of yaw resonance. The equilibrium position of the yaw resonance is the extension of the velocity vector at the “heading point,” where the vehicle median line is perpendicular to the turning radius in a steady state turn. Toward this position, the center of percussion at the rear-end of the vehicle travels. This travel is the restoration of yaw. Observing the vehicle behavior from the earth-fixed coordinate system at the moment when the heading point changes direction of travel, the heading point and the center of percussion travel in their respective directions. Each motion continues for a distance from the rear wheel to the heading point to reach the equilibrium position. This continues time equals “yaw lead time constant.” Therefore, when the yaw lead time constant is small, the vehicle is restored in a short time.

Hideki Sakai

Open Access

Testing Urban Interaction Scenarios Between Automated Vehicles and Vulnerable Road Users Using a Vehicle-in-The-Loop Test Bench and a Motion Laboratory

For testing and analyzing urban vehicle-pedestrian or vehicle-bicyclist interactions as realistically and safely as possible, new types of test environments are required. This paper presents and analyzes a test environment in which a Vehicle-in-the-Loop test bench is combined with a motion laboratory. In this test setup, a real automated vehicle can interact with a human pedestrian or cyclist in a virtual test field in the same way as in real road traffic. Employing a walking platform ensures that the pedestrian’s range of movement is not restricted during the test. This test environment is used to stimulate vehicle perception with a pedestrian avatar animated by a test subject. The test results are compared with measurements taken with a human pedestrian in reality, and strengths and weaknesses of the approach are discussed.

Michael Kaiser, Lisa Marie Otto, Steffen Müller, André Hartwecker, Christian Schyr

Open Access

Robust Inverse Vehicle Map Regression Based on Laplace Distribution

This paper deals with the identification of the relationship between vehicle acceleration and driver available actuators. The vehicle is modeled based on how a driving task is performed. The model is constructed using neural networks whose weights are identified using data collected through non tailored driving sessions. To take into account disturbances, the model follows a Laplace distribution. This leads to a more robust estimate of the vehicle knowledge and the confidence we have in it. The approach is illustrated on a prototype vehicle equipped with a petrol engine, plus a device to actuate the pedals.

Maxime Penet, Gaetan Le Gall

Open Access

Energy-Efficient Optimal Torque Vectoring for a Four-Motor High-Performance Electric Vehicle

The paper presents and compares an optimal control allocation (CA) and model predictive control (MPC)-based torque vectoring (TV) for improved energy efficiency of electric vehicle with four independent electric motors. Offline and online (instantaneous) optimisation-based CA are designed for front-rear torque distribution. For overall wheel torque allocation, a production-ready MPC-based TV is extended with energy consumption minimisation terms. CA and MPC rely on power loss curves of differently sized front and rear powertrains that are fitted with polynomial regression models. Performance of both strategies is evaluated in high-fidelity nonlinear simulation environment in terms of energy efficiency improvement on standard driving cycles and impact on the vehicle dynamics in lateral manoeuvres. Results demonstrate consistent reduction of the energy consumption and preservation of the vehicle handling behaviour.

Mattéo Prost, Ivan Cvok, Efstathios Velenis

Open Access

A Lateral Control Based on Physics Informed Neural Networks for Autonomous Vehicles

In the paper, a lateral control strategy is presented using Physics-Informed Neural Network (PINN) for automated vehicles. The main idea is that the physics information is incorporated into the training process, which leads to an improvement in the performance level of the control algorithm. Moreover, in the highly nonlinear range of the lateral dynamics, which is not properly covered by the training dataset, the stability of the vehicle is guaranteed. The results are compared to a conventional neural network trained to control the vehicle.

Tamás Hegedűs, Dániel Fényes, Balázs Németh, Vu Van Tan, Péter Gáspár

Open Access

Enhancing Electric Vehicle Remaining Range Prediction Through Machine Learning

Many automakers are announcing electric vehicle (EV) models in response to environmental regulations. However, charging times still exceed those of traditional internal combustion engine vehicles. Moreover, the supply of electric vehicle charging stations has not kept pace with the rapid expansion of electric vehicle adoption. This incongruity raises ongoing concerns for drivers regarding Distance To Empty (DTE) or remaining range. However, accurate DTE prediction faces challenges due to various factors. Therefore, predicting effective remaining range is attracting researchers’ attention. However, most algorithms are based on long-term historical driving data, which presents limitations in a shared vehicle scenario with frequently changing drivers. To address these challenges, this paper introduces a novel algorithm employing machine learning to classify driving styles and predict remaining range. This approach can integrate expected future road information and current driving conditions, offering a solution to the uncertainties associated with traditional methods.

Byunggun Kim, Haeyoun Kim

Open Access

Fuel Economy Assessment of MPC-ACC on Powertrain Testbed

The development and testing of Advanced Driver Assistance Systems (ADAS) is one of the most active fields in the automotive industry towards Automated Driving (AD). This work presents the deployment and testing of an Adaptive Cruise Control (ACC) based on Model Predictive Control (MPC). The goal is to design and validate through the experimental campaign a computationally efficient longitudinal dynamics controller and assess its fuel economy potential. The development of the control structure as well as the definition of the testing method for energy efficiency assessment are central aspects of this work. The performance of the approach is tested on a light-duty commercial vehicle on a state-of-the-art 4-axis powertrain testbed. The findings demonstrate that the speed profile can be optimized to achieve a fuel reduction of up to $$13\%$$ 13 % while maintaining mission timing and comfort.

Stefano Favelli, Luis M. Castellanos Molina, Alessandro Mancarella, Omar Marello, Eugenio Tramacere, Raffaele Manca, Mario Silvagni, Andrea Tonoli, Nicola Amati

Open Access

MLIO: Multiple LiDARs and Inertial Odometry

With the decreasing cost of LiDAR sensors, sensor setups with multiple LiDARs are becoming available. In such advanced setups with multiple LiDARs the sensor temporal asynchronicity and spatial miscalibration are critical factors for vehicle localization increasing measurement uncertainty. Hence, simple merging of synchronized point clouds as done in some literature can lead to sub-optimal results. To tackle this problem we propose MLIO, a factor graph-based odometry computation algorithm that fuses multiple LiDARs with an inertial measurement unit (IMU) and provides an accurate solution mitigating the effect of temporal asynchronisity and spatial miscalibration.The proposed algorithm is validated using a custom dataset. We compare the proposed algorithm with the state-of-the-art LiDAR-only odometry algorithms, such as KISS-ICP, and LiDAR-IMU fusion LIO-SAM and demonstrate its superiority. We were able to achieve up to 40% and 16% increment in positional and orientation accuracy compared to KISS-ICP and 25% increment in positional accuracy compared to LIO-SAM.

Pragyan Dahal, Stefano Arrigoni, Mario Bijelic, Francesco Braghin

Open Access

Vehicle State Estimation Through Dynamics Modeled Factor Graph

Ego Vehicle state estimation is integral to every autonomous driving software stack. Thereby, the estimation of the state and its components as for example the side slip angle, is a crucial component to track the vehicle maneuvers. In the absence of a direct sensor measuring side slip angle, most of the existing literature either use observers like Kalman Filters or non-modular factor graphs by modeling lateral dynamics. However, the modularity of such graphs, to integrate multiple asynchronous sensors that provide disentangled measurements, like LiDAR, GNSS, and IMU is still overlooked in the literature. In this work, we propose a novel factor graph-based architecture that builds upon the vehicle dynamics at its core to enable the fusion of multiple sensors asynchronously and enables to perform robust and accurate state estimation.We validate the proposed algorithm against two baselines, a model-based Extended Kalman Filter and a factor graph-based state estimator that uses the IMU pre-integration factor as a reference factor. The algorithms are validated in a custom dataset collected using an in-house vehicle.

Pragyan Dahal, Stefano Arrigoni, Mario Bijelic, Francesco Braghin

Open Access

Nonlinear Model Predictive Control for Enhanced Path Tracking and Autonomous Drifting Through Direct Yaw Moment Control and Rear-Wheel-Steering

Path tracking (PT) controllers capable of replicating race driving techniques, such as drifting beyond the limits of handling, have the potential of enhancing active safety in critical conditions. This paper presents a nonlinear model predictive control (NMPC) approach that integrates multiple actuation methods, namely four-wheel-steering, longitudinal tyre force distribution, and direct yaw moment control, to execute drifting when this is beneficial for PT in emergency scenarios. Simulation results of challenging manoeuvres, based on an experimentally validated vehicle model, highlight the substantial PT performance improvements brought by: i) vehicle operation outside the envelope enforced by the current generation of stability controllers; and ii) the integrated control of multiple actuators.

Gaetano Tavolo, Pietro Stano, Davide Tavernini, Umberto Montanaro, Manuela Tufo, Giovanni Fiengo, Pietro Perlo, Aldo Sorniotti

Open Access

Powerslide Control with Deep Reinforcement Learning

Controlling a vehicle’s powerslide motion in the presence of a human driver is a challenging control task, but one that may have a significant impact on vehicle safety, for example, during rapid evasive manoeuvres. Reinforcement Learning, a data-driven optimal control strategy, has gained increasing attention in recent years, demonstrating its effectiveness in successfully controlling various nonlinear systems. In this work, a novel powerslide controller is designed for an all-wheel drive battery electric vehicle with individually driven front and rear axles and a human driver in closed-loop using Reinforcement Learning. The performance of the proposed controller is analysed, and its robustness to steering disturbances and changes in road friction is demonstrated.

Florian Jaumann, Tobias Schuster, Michael Unterreiner, Torben Gräber, Johannes Edelmann, Manfred Plöchl

Open Access

Simulating Effects of Suspension Damper Degradation on Common Sensor Signals for Diagnosis Models in the Context of Condition-Based Maintenance

Degraded suspension dampers strongly influence vehicle safety and ride comfort, but often occur after several years of operation. Related workshop checks are usually not degradation-adaptive, so they can be significantly delayed to the need for maintenance. To make the maintenance adaptive to degradations, onboard diagnosis methods can be used, which rely on the degradation status extracted from sensor signals.To support the development of sensitive yet robust diagnosis models, a model that can simulate and explain the effects of damper degradation in common sensor signals is proposed. This paper focuses on low-frequency effects in signals of the wheel speed sensors, which are ultra low cost and always available in modern vehicles. As a result, the model shows a good qualitative match to real-world test drives, specifically in the frequency domain. Therefore, various real-world measurements were conducted, in particular, test bench measurements of degraded dampers and vehicle on-road tests.

Lorenz Ott, Torben Gräber, Michael Unterreiner, Johannes Edelmann, Manfred Plöchl

Open Access

Controllability of Steer-by-Wire Steering Angle Faults at the Limits of Driving Dynamics

To ensure functional safety of vehicle dynamics controllers, monitoring functions are used to limit the effectiveness of lateral dynamic inputs to a safe, controllable level. For this purpose, driving situation dependent limits for maximum permissible lateral dynamic inputs are determined with the help of subject studies. To exploratively investigate limits of a steer-by-wire superposition function in a nonlinear driving situation, a subject study (N = 52) was conducted in a semi-dynamic driving simulator. This paper presents the study design and discusses the results obtained. A 4 × 4 within subject design including an additional baseline condition was used to investigate the independent variables steering angle fault and side slip angle. First, significant effects on the maximum lateral deviation and the integral of the side slip angle are demonstrated with a two-way ANOVA, thus proving the methodical approach. Second, limits for permissible additional steering input of a steer-by-wire system in an oversteering driving situation are determined from the obtained data.

Janick Birkemeyer, Lukas Borkowski, Ingo Wülfing, Steffen Müller

Open Access

An Automated Lane-Change System Based on Probabilistic Trajectory Prediction Network

In highway driving, understanding the intentions of surrounding vehicles is a crucial prerequisite to ensure collision-free lane changes. In this study, an automated lane change system framework is proposed for highway driving. A Long Short-Term Memory (LSTM)-based network is utilized to predict the paths of surrounding vehicles as probability distributions. When initiating a lane change, multiple candidate paths are generated, and the collision probability is then calculated by considering the generated paths of the host vehicle and the predicted paths of surrounding vehicles. Using the vehicle as a reference, the collision risk area is defined first related to the lane change. Secondly, the probability of the predicted distribution of the surrounding vehicles existing within this area is integrated to derive the collision probability. Subsequently, the collision-free optimal path is adopted, and Model Predictive Control (MPC) is employed for path tracking. The proposed framework was validated on a highway-like proving ground.

Yoonyong Ahn, Sangwon Han, Jihoon Sung, Jaeho Choi, Kunsoo Huh

Open Access

Development of Vehicle State Estimation Method for Dedicated Sensor-Less Semi-active Suspension Using AI Technology

This paper presents a sensor-less vehicle state estimation method using a neural network for semi-active suspensions. This method surpasses conventional mathematical models in performance and reduces calibration effort. The developed system, logic, and learning method are designed to address AI-specific challenges such as increased processing load and learning techniques, and their performance is validated through simulations and real-world tests. The results show that this system performs on par with those using dedicated sensors.

Yoshifumi Kawasaki, Akai Akihito, Ryusuke Hirao

Open Access

Validation of Control Method to Improve Posture Stability of Narrow Tilting Vehicles Using Real-Vehicle Dynamic Tests

Narrow Tilting Vehicles (NTVs) have been proposed as a solution to traffic problems such as congestion and limited parking spaces. However, their small footprint can lead to a reduction in postural stability. To address this issue, NTVs have been designed to reduce the lateral acceleration experienced by occupants by tilting the vehicle body inwards during turns, depending on the driving situation. Three types of tilt control methods have been proposed for NTVs: direct tilt control (DTC), steering tilt control (STC), and combined steering and direct tilt control (SDTC). In this study, we proposed a simplified method to control SDTC using a single roll directional motion equation, focusing on the yaw angular acceleration and roll inertia of the vehicle body. The proposed method was found to result in a 84% reduction in the lateral acceleration experienced by occupants during turning maneuvers in the evaluation of real-vehicle dynamic tests.

Keizo Araki, Jongseong Gwak, Yoshihiro Suda

Open Access

Gradient Correction for Asynchronous Stochastic Gradient Descent in Reinforcement Learning

Distributed stochastic gradient descent techniques have gained significant attention in recent years as a prevalent approach for reinforcement learning. Current distributed learning predominantly employs synchronous or asynchronous training strategies. While the asynchronous scheme avoids idle computing resources present in synchronous methods, it grapples with the stale gradient issue. This paper introduces a novel gradient correction algorithm aimed at alleviating the stale gradient problem. By leveraging second-order information within the worker node and incorporating current parameters from both the worker and server nodes, the gradient correction algorithm yields a refined gradient closer to the desired value. Initially, we outline the challenges associated with asynchronous update schemes and derive a gradient correction algorithm employing local second-order approximations. Subsequently, we propose an asynchronous training scheme incorporating gradient correction within the generalized policy iteration framework. Lastly, in the context of trajectory tracking tasks, we compare the impact of employing gradient correction versus its absence in an asynchronous update scheme. Simulation results underscore the superiority of our proposed training scheme, demonstrating notably faster convergence and higher policy performance compared to the existing asynchronous update methods.

Jiaxin Gao, Yao Lyu, Wenxuan Wang, Yuming Yin, Fei Ma, Shengbo Eben Li

Open Access

Indoor Test-Rig to Measure the Lateral Characteristics of Bicycle Tyres

Tyre characteristics can strongly affect bicycle dynamics, therefore the overall bicycle performances. However, it may be hard to measure lateral characteristics with low uncertainty. Proper test-rigs are needed to obtain reliable tyre parameters, to be used then for modelling. The paper presents VeTyT, acronym of “Velo Tyre Testing”, a new test-rig specifically developed for bicycle tyres at the Department of Mechanical Engineering of Politecnico di Milano. It is the first test-rig for bicycle tyres in compliance with the standard ISO 9001-2015. We also present the results of an experimental campaign conducted on a road racing bicycle tyre. In particular, the impact of rim stiffness is relevant to tyre characteristics, leading to a 13% increase in cornering stiffness under the same test conditions.

Gabriele Dell’Orto, Giampiero Mastinu

Open Access

Driver Behaviour Characterization Using an Instrumented Steering Wheel Conscious/Unconscious Muscle Activation

The paper presents an Instrumented Steering Wheel (ISW) that measures the forces and moments that the driver applies at each hand. The ISW is equipped with two six-axis load cells and has the same inertia of a reference steering wheel fitted on normal production cars. The ISW is used to assess the conscious or unconscious application of forces at the steering wheel during a set of manoeuvres. The conscious steering actions refer both to sinusoidal steering input and handling circuit driving. A statistical analysis is performed to characterize the conscious driver behaviour. Referring to unconscious activation of muscles, a kick-plate pass-by manoeuvre is studied. Several drivers are employed for such tests. Typical behavioural patterns are found, describing how drivers apply forces and moments in conscious steering actions. The unconscious moment applied during a kick-plate manoeuvre may be even 20% of the maximum torque applied during counter-steering. The results can be used to develop new driver models, additionally, the ISW is proposed as a tool for properly defining the ADAS intervention logic to reduce the intrusiveness feeling felt by the driver and increase safety in case of unconscious steering actions.

Xabier Carrera Akutain, Francesco Comolli, Massimiliano Gobbi, Giampiero Mastinu, Giovanni Radaelli

Open Access

Interaction Between L4 AVs and Human Drivers in Italian Take-over Scenarios

Automated Driving (AD) technologies are rapidly transforming road transportation, emphasizing the critical role of Human-Machine Interaction (HMI). In this regard, the paper examines the interaction between Level 4 Autonomous Vehicles (L4 AVs) and human drivers in take-over scenarios within Italian traffic environments. Employing the Dynamic Driving Simulator at Politecnico di Milano, the study presents two simulation environments: an urban roundabout and a Ligurian highway. The research aims to measure the driver response during take-over requests. Questionnaires are used to psychologically analyse the participants. Physiological signals, including ECG, EEG, and EDA, are acquired throughout the entire simulation.

Linda Boscaro, Veronica De Guglielmo, Andrea Fossati, Andrea Galbiati, Massimiliano Gobbi, Gianpiero Mastinu, Giorgio Previati, Edoardo Sabbioni, Maria Gabriella Signorini, Antonella Somma, Luca Subitoni, Lorenzo Uccello

Open Access

A Moving Laboratory for Automotive Components Safety Testing (MoLAS)

The MoLAS is a moving laboratory able to fully characterize the tire behavior in the real working environment. The basic structure of the moving laboratory is represented by a semi-trailer. A rotating frame that supports the measuring system is used to set the camber level by using an electric actuator. An electric steering actuation system guarantees high output torque and high output power. A pneumatic actuator is used to apply the vertical load, allowing to generate a large range of vertical force up to SUVs values. A complex driveline including an internal combustion engine (ICE) coupled with the gearbox and an electromagnetic (EM) retarder is used to apply both driving and braking torques. This aspect constitutes a new feature of the system.

A. Biffi, F. Ballo, M. Gobbi, G. Mastinu

Open Access

Force Sensors for the Active Safety of Road Vehicles

Force and moment measurement within road vehicles plays a break-through role in automotive engineering. Both wheel force transducers and instrumented hub carriers are considered in the paper. Both technologies have advantages and disadvantages. Active safety systems (ABS, ESP, up to full automated driving) are expected to be impacted by the measurement of forces and moments at the wheels. Friction potential evaluation and driver model development and monitoring are major field of research. Force and moment measurement technology may also be exploited for lightweight construction purposes. Promising technologies are the ones that don’t need RF data transfer, providing low latency for data transfer and are resiliency against cyber-attacks.

M. Milivinti, M. Amadini, F. Ballo, M. Gobbi, G. Mastinu

Open Access

Design of Explicit and Lateral-Longitudinal Integrated Motion Controller with Safety Guarantee for Autonomous Vehicles

Model predictive control (MPC) is an effective method in lateral-longitudinal integrated control with safety guarantee for autonomous vehicles. But its computational burden is significant, making it challenging to meet real-time requirements. The contribution of this paper is to propose an explicitly solvable autonomous vehicle motion controller with lateral-longitudinal integrated characteristics and safety guarantee, achieved by integrating input-output controllers from exponential control Lyapunov function (ECLF) and exponential control barrier function (ECBF). We performed simulations using a high-fidelity dynamics model in CarSim for simple urban traffic scenarios. The simulation results demonstrate that our designed controller has good trajectory tracking performance, safety guarantee, low computational burden and a certain level of robustness.

Haoyu Gao, Chang Liu, Yingxi Piao, Sen Yang, Beiyan Jiang, Shengbo Eben Li

Open Access

Towards Automated Driving: Findings and Comparison with ADAS

The present contribution provides preliminary empirical findings concerning the first-in-literature independent experimental characterization of a commercially viable automated driving system in Europe. In particular, the paper reports on the testing campaign involving a type-approved Automated Lane Keeping System (ALKS) equipped vehicle and its comparison with the comparable driving assistance feature technology from the same vehicle: the Adaptive Cruise Control (ACC) system. The results suggest that the ALKS shows substantially enhanced performances with respect to ACC. In particular, both the string stability metrics and the reaction time show remarkable improvements. Additionally, the increased stability is not obtained via resorting to a significantly higher time gap which further motivates that string stable car-following is indeed feasible.

Riccardo Donà, Konstantinos Mattas, Giovanni Albano, Sandor Váss, Biagio Ciuffo

Open Access

Stability Issues in Adaptive Cruise Control Systems and Traffic Implication

Adaptive Cruise Control (ACC) systems under short headway configurations have been found to have a potentially detrimental impact on the transport network due to the string instability effect. Such phenomenon results in traffic perturbations amplification downstream causing increasing fuel consumption and posing safety threats. However, recent findings summarized in this paper show how even the simpler platoon stability might not be attained with current ACC-equipped vehicles raising additional concerns regarding their unregulated operation. In fact, as part of a recent campaign involving state-of-the-art assisted vehicles, an ACC displayed a low-frequency oscillatory behavior around the equilibrium speed. This work, by leveraging a mixed simulation/empirical approach, uncovers the harmful influences of such behavior. Ultimately, we found that despite the poor stability phenomenon might not be impactful for one vehicle, the overall repercussions on the transportation network can be dramatically detrimental raising the need for a regulatory framework for lower-level automation.

Riccardo Donà, Konstantinos Mattas, Giovanni Albano, Sandor Váss, Biagio Ciuffo

Open Access

The Influence of Transient Tire Force Transmission on Sideslip Angle Estimation

Knowledge of the sideslip angle is significant in development, testing and validation of advanced driver assistance systems and vehicle control systems, to enhance vehicle performance and stability. This article investigates the influence of having additional information about transient tire behavior during estimation of the sideslip angle. Since transient behavior depends on the dynamic excitation, the effect during different driving maneuvers (excitations) such as steady-state cornering, slalom and general handling as well as on different road surfaces is investigated. The results show that considering transient force transmission even by a pragmatic approach leads to a significant improvement.

Dženana Puščul, Martin Schabauer, Cornelia Lex

Open Access

AI-Based Power Split Strategy for Hybrid Commercial Vehicle Applications

In the rapidly evolving landscape of hybrid commercial vehicle technology, integrating artificial intelligence (AI) with fuel cell applications offers a promising frontier for efficient, sustainable and eco-friendly road freight system. There has been many different approaches on optimization of power split between the electric motor and the fuel cell system (FCS). Conventional approaches use quadratic optimization to determine the optimal power from the electric motor at each discrete grid point along the route, with initial and final battery state of charge (SoC) as constraints. This paper proposes a deep reinforcement learning-based approach to optimize the power split between the electric motor and the FCS in a hybrid vehicle at every time point during the vehicle’s trip. The agent demonstrated the ability to autonomously learn and improve power split decisions, resulting in enhanced fuel efficiency.

Pratheesh Sivaraman Nair, Tomislav Bukic, Dominik Burnner, Georgios Koutroulis, Milan Zivadinovic

Open Access

Risk-Predictive Path Planning in Urban Autonomous Driving: A Geometric Approach to VRU Crossing

This research introduces a method for autonomous driving systems to safely overtake bicycles in urban environments. It identifies the risk of sudden crossing when overtaking a bicycle and proposes a solution to minimize this risk. The method classifies the situation into three conditions related to the drivable space of the road and speed of the bicycle, and it determines the target speed and positions. The proposed algorithm uses onboard sensor information and assumptions of the bicycle’s virtual sudden-crossing motion, enabling real-time calculations in a practical environment. Simulations demonstrate the method’s effectiveness, showing it can generate a natural overtaking speed and lateral gap based on road width and bicycle speed. The proposed method is compatible with waypoint-based path generation methods we have proposed in previous research, making it a promising solution for future autonomous driving systems. Future research will discuss the method’s implementation in automated vehicles, contributing to safer and more efficient autonomous driving systems.

Yohei Fujinami, Pongsathorn Raksincharoensak
Backmatter
Metadata
Title
16th International Symposium on Advanced Vehicle Control
Editors
Giampiero Mastinu
Francesco Braghin
Federico Cheli
Matteo Corno
Sergio M. Savaresi
Copyright Year
2024
Electronic ISBN
978-3-031-70392-8
Print ISBN
978-3-031-70391-1
DOI
https://doi.org/10.1007/978-3-031-70392-8

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