Advanced Microsystems for Automotive Applications 2017

Consumer electronics such as smartphones, tablets, and wearables are part of our everyday life—visible everywhere and taken for granted. Less visible however are the small MEMS (micro-electromechanical systems) sensors that are an integral part of these devices. Smart sensor technology enables things to be sensed and connected—in all parts of our daily life, in homes, vehicles, cities. With the emergence of the Internet of Things (IoT), more and more devices become connected which results in demanding challenges for MEMS sensor technology providers—in addition to the trends of low cost, small size, low power consumption as well as overall system performance. The exciting developments in the IoT are advancing at an amazing pace. It is not just about how devices communicate or sense their surrounding environments, but increasingly about how technology interacts with human beings. Laser-projected virtual interfaces based on optical MEMS are a new fascinating solution in a world of previously unimaginable opportunities. They give any kind of device a unique personality of its own, enabling technology to interact with people, to make life simpler and more exciting. It is a ground-breaking solution for embedded projectors and augmented reality applications such as games, infotainment as well as in-car head-up displays or intelligent head lamps for automated driving.

When electronic equipment is used in harsh environments with long expected lifetime, there is a need to understand that environment more in detail. This situation is today a reality for many application areas including the automotive sector, heavy industry, the defense sector, and more. To fully understand the working environment, a unit has been developed to monitor physical data such as temperature, vibration, humidity, condensation, etc., to be used in the product development phase for new products. This paper presents the underlying principles for the ESU (Environmental Supervision Unit) and details on the design.

This paper presents a Synthetic Aperture Radar (SAR) system for automotive applications. The focus is on the use of current series-sensor technology. Typical sensors’ parameters are discussed and their effect on SAR image quality is displayed. A complete model for automotive SAR calculation is also presented. Critical properties of SAR in combination with automotive setups are illustrated. Measurements and simulations for a parking-lot scenario created with that model are shown. The system is able to distinguish between cars and parking slots.

In this contribution, we present the concept of a 4×128 pixel line sensor for direct time-of-flight measurement based on single-photon avalanche diodes (SPAD) fabricated in a high-voltage automotive 0.35 μm CMOS process. An in-pixel time-to-digital converter with a resolution of 312.5 ps determines the arrival of photons reflected from targets in the area of view. Since we are employing a so-called first photon approach, there are no dead-time effects. In addition, our approach uses a variable photon coincidence detection to suppress effects of ambient illumination. As a test vehicle we have implemented a 1×80 pixel CMOS SPAD line sensor and characterized it.

The range of applications for autonomous guided carts (AGC) is increasingly growing. Especially in industrial environments ensuring high safety standards in combination with high availability and flexibility are major requirements. For this reason, knowledge about its own position in the environments becomes particularly important. For AGC with low vehicle height localization approaches based on contour observations are widespread. However, in over-time-changing environments the robustness of these techniques is limited. This paper proposes an approach for updating the underlying map in real time during operation. This map update allows for a long-term robust localization. The proposed approach is evaluated for a dynamic test scenario using a cellular transport vehicle.

The work presented aims at an early and reliable prediction of lane change maneuvers intended by the driver. For that purpose, an artificial neural network is proposed fusing features modeling the environmental situation that influences the formation of intentions, the gaze behavior of the driver preparing an intended maneuver and the movement of the vehicle. The sensor data required are provided by a multisensor setup comprising automotive radar and camera sensors. The whole prediction algorithm was put into practice as a real-time application and was integrated in a test vehicle. With this system, a naturalistic driving study was conducted on urban roads. The naturalistic driving data obtained were finally used for the parametrization of the algorithm by means of machine learning and for the evaluation of the prediction performance of the algorithm, respectively.

As road edge information gives various benefits to ADAS (Advanced Driver Assistance Systems) and AD (Autonomous Driving) applications, we have developed a road edge detection algorithm with stereo camera. In this paper, two ADAS/AD applications using road edge information, validated by simulation and experiment, are introduced to show benefits of the method. An integrated lateral assist system including LDP (Load Departure Prevention system) and RDP (Road Departure Prevention system), which assumes lane support system in Euro NCAP, is proposed as an ADAS application. Also, “obstacle avoidance through road shoulder space” is introduced as an application for AD.

A recursive least squares (RLS) based observer for simultaneous estimation of vehicle mass and road grade, using longitudinal vehicle dynamics, is presented. In order to achieve robustness to unknown disturbances and varying parameters, depth is chosen in a sufficient way. This is done with a sensitivity analysis, identifying parameters with significant influence on the estimation result. The identification of vehicle parameters is presented in detail. The method is validated with an all-electric vehicle (AEV) using natural driving cycles. The results show little deviation between estimation and reference, as well as good convergence in urban areas, providing sufficient excitation. However, on highway roads, environmental influences like wind and slipstream of trucks, worsen the results, especially in combination with little excitation for the observer.

We propose a method for estimating the vanishing point of structured roads directly in the image plane using the parallel nature of road markings as well as intelligent preprocessing and data reduction steps. The resulting vanishing point enables estimating the image to world projection, which then is used to perform subsequent tasks such as object detection. The major advantages of the proposed method are modest computational requirements as well as independence of the used camera model and without a calibration phase.

Predictive motion planning is a key for achieving energy-efficient driving, which is one of the major visions of automated driving nowadays. Motion planning is a challenging task, especially in the presence of other dynamic traffic participants. Two main issues have to be addressed. First, for globally optimal driving, the entire trip has to be considered at once. Second, the movement of other traffic participants is usually not known in advance. Both issues lead to increased computational effort. The length of the prediction horizon is usually large and the problem of unknown future movement of other traffic participants usually requires frequent replanning. This work proposes a novel motion planning approach for vehicles operating in dynamic environments. The above-mentioned problems are addressed by splitting the planning into a strategic planning part and situation-dependent replanning part. Strategic planning is done without considering other dynamic participants and is reused later in order to lower the computational effort during replanning phase.

In this paper, we present our approach to data generation for the training of neural networks in order to achieve semantic segmentation in an autonomous environment. Using a small set of previously labeled images, this approach allows to automatically increase the amount of training data available. This is achieved by recombining parts of the images, while keeping the overall structure of the scene intact. Doing so allows for early network training, even with only few training samples at hand. Furthermore, first results show that training networks using the so created datasets allow for good segmentation results when compared to publicly available datasets.

Future smart vehicles will employ automotive over-the-air updates to update the soft ware in the embedded electronic control units. The update process can affect the safety of the involved users, thus requires a comprehensive and elaborate security architecture ensuring the confidentiality and the integrity of the exchanged data, as well as protecting the privacy of the involved users. In this paper, we propose an automotive security architecture employing Blockchain to tackle the implicated security and privacy challenges. We describe our proof-of-concept implementation of a Blockchain-based software update system, use it to show the applicability of our architecture for automotive systems, and evaluate different aspects of our architecture.

The open and cooperative nature of Cyber-Physical Systems (CPS) poses new challenges in assuring dependability. The DEIS project (Dependability Engineering Innovation for automotive CPS. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732242, see http://​www.​deis-project.​eu) addresses these challenges by developing technologies that form a science of dependable system integration. In the core of these technologies lies the concept of a Digital Dependability Identity (DDI) of a component or system. DDIs are modular, composable, and executable in the field facilitating (a) efficient synthesis of component and system dependability information over the supply chain and (b) effective evaluation of this information in-the-field for safe and secure composition of highly distributed and autonomous CPS. The paper outlines the DDI concept and opportunities for application in four industrial use cases.

The current developments in automotive industry toward automated driving require a massive increase in functionality, number, and complexity of the electronic systems. At the same time, the functional safety of those electronic systems must be improved beyond the high requirements applied today already. Designing the systems for a guaranteed lifetime on statistical average will no longer suffice. Therefore, new methods in the design and reliability assessment toward maintainable or replaceable systems are required. Prognostics and health management (PHM) provides the way for this upgrade in reliability methodology. The paper introduces a multi-level PHM strategy based on smart sensors and detectors integrated into the functional electronic units so that maintenance can be triggered if needed yet always well before the actual failure occurs in the individual system.

In this paper, we will explore challenges for the validation and testing of Automated Driving Functions (ADF), which represent one of the major roadblocks for successful integration of emerging technologies into commercial vehicles. We provide an overview of current methodologies used for validation and testing, focusing on the missing parts. Furthermore, we give an insight into promising methodologies, frameworks, and research areas which aim to reduce current testing and validation efforts.

Within the last decade, several innovations in the automotive domain were introduced in the field of driver-assistance systems (DAS). As technology rapidly advances toward automated driving this trend further continues, integrating more intelligent, interconnected, and complex functionality. This results in a constantly expanding space of system states that need to be validated and verified. Approaches for virtualization of test drives such as X-in-the-loop (XiL) are in focus of current research and development. In this contribution, we introduce a concept for automated quality assessment within a randomized digital test drive. Our aim is to analyze and assess the continuous behavior of automotive systems during multiple realistic traffic scenarios within a simulated environment. Therefore an analysis and comparison of current test approaches and their verification and validation goals are conducted. These results are utilized to derive requirements and constraints for an automated assessment. In comparison to established systematic test approaches, our concept based on a continuous assessment of the entire test drive constituting multiple driving scenarios. To consider the continuous behavior and parallel assessment of different functionality, a distinction between activation conditions and test conditions is conducted. Additionally, the hierarchization of conditions allows identification and evaluation on different abstraction levels. We include general assessments in addition to system and function-specific behavioral assessments. The approach is elaborated on an example use case of an Adaptive Cruise Control (ACC) system.

Advanced Driver-Assistance Systems (ADAS) and Automated Driving (AD) vehicles rely on a variety of sensors and among them optical sensors. Extensive testing of functions using optical sensors is required and typically performed at proving grounds like AstaZero. Soft surrogate targets are used for safety reasons but the optical and geometrical characteristics of soft car targets may differ considerably from that of real vehicles. During tests the quality of the soft car targets deteriorates due to repeated impacts and reassembly of the targets, and there is a need of methods for securing the quality of the soft car targets over time. One of the main goals of the HiFi Visual Target project is to develop and validate accurate and repeatable measurement methods of the optical and geometric characteristics of soft car targets.

In the next decades, road transport will undergo a deep transformation with the advent of connected and automated vehicles (CAVs), which promise to drastically change the way we commute. CAVs hold significant potential to positively affect traffic flows, air pollution, energy use, productivity, comfort, and mobility. On the other hand, there is an increasing number of sources and reports highlighting potential problems that may arise with CAVs, such as, conservative driving (relaxed thresholds), problematic interaction with human-driven vehicles (inability to take decisions based on eye contact or body language) and increased traffic demand. Therefore, it is of high importance to assess vehicle automated functionalities in a case-study simulation. The scope of this paper is to present some preliminary results regarding the impact assessment of cooperative adaptive cruise control (CACC) on the case-study of the ring road of Antwerp, which is responsible for almost 50% of the traffic and pollution of the city. Scenarios with various penetration rates and traffic demands were simulated showing that coordination of vehicles may be needed to significantly reduce traffic congestion and energy use.

Germany has very recently adopted the allegedly most advanced road traffic law in the world. At first glance, drivers will be allowed to pursue non-driving-related activities during phases of automation such as surfing the internet or watching films. This paper shows, however, that the new law gives rise to significant legal uncertainty regarding human–machine interaction in automated vehicles, with liability risks emanating therefrom both for manufacturers and users of the new technology. This paper puts forward suggestions as to how these legal risks can be addressed when designing and implementing the human–machine interface. Automation systems will only find general acceptance if they can be brought to market without unduly burdening manufacturers and users from a liability perspective.

Connectivity is one of the major prerequisites of automated driving. Enabled by numerous connected sensors, new cars offer new functionalities, provide higher security levels and promise to enhance the comfort of travelling. However, by connecting a vehicle with its environment, the car becomes more transparent. The integration of the car into a smart grid seems to conflict with the users’ expectation of their car as a private retreat, thus reducing the acceptance and usage adoption of connected cars. This article aims at helping developers and engineers to consider the user’s expectations when designing a connected car. Furthermore, this article reviews and compares recent international surveys on user’s privacy with our own results on the user’s attitude towards connected vehicular services.