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About this book

This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation.
The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF.
Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.

Table of Contents

Frontmatter

Chapter 1. Cooperation and the Role of Autonomy in Automated Driving

While automation in human–machine systems can increase safety and comfort, the 2016 lethal crash of an automated vehicle demonstrates that automation is not without its risk. Indeed, accidents from the air traffic domain also demonstrate that cooperation between human and machine is crucial and interaction design must be devoted great care. Therefore, the present paper aims at developing design recommendations to reduce the risks of lethal crashes with automated vehicles. To this end, the concepts of cooperation and autonomy are closely investigated. These two terms are central to research on human machine cooperation; however, the present definition of cooperation and the role of autonomy might be further specified for the domain of automated driving. Therefore, selected perspectives from different scientific fields (e.g., sociology and psychology) will be presented in order to develop a differentially inspired working definition of cooperation, which is tailored to the automated driving domain. Another goal of this approach is to investigate different views on the concept of autonomy, which is often entailed in work on cooperation. This can help clarify the role of autonomy in automated driving in particular. Moreover, insights from the presented theories and findings on cooperation can be transferred to the interaction design of automated vehicles. Accordingly, recommendations for interaction design will be presented. Finally, an example for the implementation of the working definition and the design recommendations will be presented by describing a prototype for automated driving—the H-mode prototype.

Gina Wessel, Eugen Altendorf, Constanze Schreck, Yigiterkut Canpolat, Frank Flemisch

Chapter 2. Robust Real-World Emissions by Integrated ADF and Powertrain Control Development

This work gives an outlook on the potential of automated driving functions (ADFs) to reduce real-world $$\mathrm {CO_2}$$ and pollutant emissions for heavy-duty powertrains. Up to now, ADF research mainly focuses on increased traffic safety, driver comfort, and road capacity. Studies on emissions are lacking. By taking the driver out-of-the-loop, cycle-to-cycle variability is removed and energy losses and large accelerations can be significantly reduced. This enhances emission performance robustness, which will allow for more fuel-efficient engine settings. A general, optimal control framework is introduced, which integrates ADF with energy and emission management. Based on predictions of the vehicle power demand and emissions, a desired vehicle velocity profile, which minimizes the overall vehicle energy consumption, is determined. In this approach, real-world tailpipe emissions are explicitly taken into account. This opens the route to emission trading on vehicle or even, on platoon, fleet, and traffic level. For the combined ADF and powertrain development, testing, and certification, various opportunities are presented to fully exploit the synergy between these systems and to reduce development time and costs. By equipping vehicles with an emission monitoring system, real-world data of the ADF emission reduction potential becomes available. As validated traffic and component aging models are lacking, this data is also valuable for realistic scenario development and uncertainty modeling in virtual or mixed testing. This will lead to improved robustness evaluation and performance.

Frank Willems, Peter van Gompel, Xander Seykens, Steven Wilkins

Chapter 3. Gaining Knowledge on Automated Driving’s Safety—The Risk-Free VAAFO Tool

While the technical development of automated driving functions has made significant progress in the past decade, there is still no validation method. An assessment of automated driving before the market launch is difficult because, except for test drives on real roads, there is no information about the behavior of automated vehicles in real traffic. Due to the high level of safety in today’s traffic, it is not economically feasible to prove the superiority of automated driving by test drives only, because it would require a testing distance of several million to billion kilometers. To shift testing to simulation or test tracks, information about critical test scenarios is necessary. The Virtual Assessment of Automation in Field Operation (VAAFO) helps to gain more knowledge about automated driving functions while using series vehicles in today’s traffic. The new function is tested virtually, and at the same time, information is gained to deduce test scenarios for testing on test tracks and simulation.

Philipp Junietz, Walther Wachenfeld, Valerij Schönemann, Kai Domhardt, Wadim Tribelhorn, Hermann Winner

Chapter 4. Statistical Model Checking for Scenario-Based Verification of ADAS

The increasing complexity of advanced driver assistant systems requires a thorough investigation of a high-dimensional input space. To alleviate this problem, simulation-based approaches have recently been proposed. An overall safety assessment, however, requires the simulation results to relate to the safety of such systems in real-world scenes. To this end, we propose a rigorous method of specifying requirements for these systems depending on the environmental situation and generating statistical evidence for the safety of the system in the specified environmental situations. We demonstrate this process in an exemplary highway scenario involving decision and perception uncertainty.

Sebastian Gerwinn, Eike Möhlmann, Anja Sieper

Chapter 5. Game Theory-Based Traffic Modeling for Calibration of Automated Driving Algorithms

Automated driving functions need to be validated and calibrated so that a self-driving car can operate safely and efficiently in a traffic environment where interactions between it and other traffic participants constantly occur. In this paper, we describe a traffic simulator capable of representing vehicle interactions in traffic developed based on a game-theoretic traffic model. We demonstrate its functionality for parameter optimization in automated driving algorithms by designing a rule-based highway driving algorithm and calibrating the parameters using the traffic simulator.

Nan Li, Mengxuan Zhang, Yildiray Yildiz, Ilya Kolmanovsky, Anouck Girard

Chapter 6. A Virtual Development and Evaluation Framework for ADAS—Case Study of a P-ACC in a Connected Environment

Advanced driver assistance systems (ADAS) or even (partially) automated driving functions (ADF) can lead to substantial improvements in fuel economy, safety, and comfort of passenger cars. Especially, in view of new technologies, such as connected vehicles, additional improvements are feasible. However, testing and validation of ADAS in a connected and interacting environment are a critical and not yet fully solved task. In real-world driving situations in a dense urban traffic environment, constant interactions between the system under test (SUT) and other traffic participants occur. The number of possible scenarios and test cases is huge and renders a case by case approach, even for function prototyping and performance evaluation, almost impossible. In this work, a virtual development framework is proposed which allows performance testing under realistic traffic conditions by taking the interaction between SUT and other participants into account. A combination of a microscopic traffic simulation and a high-detailed vehicle simulation is utilized. To handle the interaction between both tools, a co-simulation framework with an interface layer for synchronization is developed which serves also as input for virtual sensors and prototype functions. The framework is demonstrated by a case study for a predictive adaptive cruise control (P-ACC) in a connected environment. This case study shows both the potential benefits of utilizing available information via new communication channels for ADAS and the applicability of the proposed framework.

Harald Waschl, Roman Schmied, Daniel Reischl, Michael Stolz

Chapter 7. A Vehicle-in-the-Loop Emulation Platform for Demonstrating Intelligent Transportation Systems

In an emerging world of large-scale, interconnected, intelligent transportation systems, demonstrating and validating novel ideas and technologies can be a challenging one. Traditionally, one is presented with a choice to make, between performing demonstrations with a few proof-of-concept “outfitted” vehicles, or experimenting with large-scale computer simulation models. In this chapter, we revisit a recent vehicle-in-the-loop (VIL) emulation platform that was developed with the goal in mind of taking steps towards countering the above validation dilemma. Roughly speaking, it was shown that a real, outfitted test vehicle, equipped with novel intelligent transportation technologies, could be “embedded” in a large-scale traffic emulation being performed with the microscopic traffic simulation package SUMO, thus allowing the real vehicle and driver to interact with thousands of simulated cars on a common road map in real-time. In our present work, we now provide an overview of the latest updates to the VIL platform, which include some enhancements to increase the platform’s versatility and improve its functionality.

Wynita Griggs, Rodrigo Ordóñez-Hurtado, Giovanni Russo, Robert Shorten

Chapter 8. Virtual Concept Development on the Example of a Motorway Chauffeur

It is well known that the development of future automated driving faces big challenges regarding testing and validation. One strategy to tackle the drastically increased complex interaction of vehicle, driver, and environment is the so-called front-loading approach. This involves virtual development of new vehicle functions enabling early stage testing and validation. Within the funded project Technology Concepts for Advanced Highly Automated Driving (TECAHAD), this front-loading approach was applied for a concept development of an automated driving system (ADS)—the Motorway Chauffeur (MWC)—fully responsible for longitudinal and lateral motion of a car on motorways. In the following, we provide an insight on early stage virtual development of this ADS. Topics range from high-level requirements and functional safety investigations to software architecture and major components of the virtual implementation. Finally, first simulation results are shown for some MWC use cases, motivating the planned future real vehicle prototype implementation.

G. Nestlinger, A. Rupp, P. Innerwinkler, H. Martin, M. Frischmann, J. Holzinger, G. Stabentheiner, M. Stolz

Chapter 9. Automation of Road Intersections Using Distributed Model Predictive Control

The automation of road intersections is increasingly considered as an inevitable next step toward a higher level of autonomy on our roads. For the particular case of fully automated vehicles, we propose a distributed model predictive control approach in which multiple agents are able to pass the intersection simultaneously while keeping a sufficient safety distance to conflicting agents. Therefore, each agent solves a local optimization problem subject to non-convex safety constraints which couple the agents. In order to handle these coupling constraints, we propose constraint prioritization. With that methodology, for two pairwise conflicting agents, the safety constraint is only imposed on the agent with lower priority which does not imply any a priori intersection passing order. Finally, we can solve the distributed optimization problem in parallel without any nested iterations. To solve the local non-convex optimization problems, we apply a semidefinite programming relaxation in combination with randomization to obtain appropriate and feasible solutions. A simulation study finally proves the efficacy of our approach.

Alexander Katriniok, Peter Kleibaum, Martina Joševski

Chapter 10. MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

This chapter presents multi-policy decision-making (MPDM): a novel approach to navigating in dynamic multi-agent environments. Rather than planning the trajectory of the robot explicitly, the planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation that captures the complex interactions between the actions of these agents. These polices capture different high-level behavior and intentions, such as driving along a lane, turning at an intersection, or following pedestrians. We present two different scenarios where MPDM has been applied successfully: an autonomous driving environment models vehicle behavior for both our vehicle and nearby vehicles and a social environment, where multiple agents or pedestrians configure a dynamic environment for autonomous robot navigation. We present extensive validation for MPDM on both scenarios, using simulated and real-world experiments.

Alex G. Cunningham, Enric Galceran, Dhanvin Mehta, Gonzalo Ferrer, Ryan M. Eustice, Edwin Olson
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