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In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account.
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include: Modern ADAS development platforms; Design space exploration;DRIVER MODELLING;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation systems
With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
Today, Advanced Driver Assistance Systems (ADAS) represent an increasing contribution to active road safety and driving comfort. Their task is to help the driver to avoid accidents or at least to minimize their consequences. Depending on their objectives they can support the driver's decisions by providing additional information (e.g. Park Distance Control, Night Vision, Traffic Sign Recognition) or even directly influence the driving process (e.g. Park Assist, Adaptive Cruise Control, Lane Assist). Since the absolute number of ADAS sensors in vehicles is permanently increasing, further development of existing sensor data processing mechanisms is required to ensure a robust functionality and (eventual) timely detection of system limits (e.g. caused by sensor misalignment, bad weather conditions, pollution or aging). Therefore, continuous knowledge of the quality of the environmental perception is of significant importance. In this thesis, general probabilistic approach to multi-sensorial environmental perception of ADAS is presented. This approach incorporates sensor data fusion with self-diagnosis capability and maneuver level intent estimation of detected objects. Thus, the quality of environmental perception can be continuously monitored and the intents of the traffic participants can be predicted. The resulting probabilities are uniform and consistent basis and reflect the reliability of the results. This knowledge is an important prerequisite for the development of future complex and robust Advanced Driver Assistance Systems. The developed concepts have been used and approved in project "Integrated Lateral Assistance", a subproject of research initiative AKTIV (abbreviation for "Adaptive and Cooperative Technologies for the Intelligent Traffic") supported by the German Federal Ministry of Economics and Technology.
Autonomous Driving and Advanced Driver-Assistance Systems (ADAS): Applications, Development, Legal Issues, and Testing outlines the latest research related to autonomous cars and advanced driver-assistance systems, including the development, testing, and verification for real-time situations of sensor fusion, sensor placement, control algorithms, and computer vision. Features: Co-edited by an experienced roboticist and author and an experienced academic Addresses the legal aspect of autonomous driving and ADAS Presents the application of ADAS in autonomous vehicle parking systems With an infinite number of real-time possibilities that need to be addressed, the methods and the examples included in this book are a valuable source of information for academic and industrial researchers, automotive companies, and suppliers.
The main topic of this book is the recent development of on-board advanced driver-assistance systems (ADAS), which we can already tell will eventually contribute to the autonomous and connected vehicles of tomorrow. With the development of automated mobility, it becomes necessary to design a series of modules which, from the data produced by on-board or remote information sources, will enable the construction of a completely automated driving system. These modules are perception, decision and action. State-of-the-art AI techniques and their potential applications in the field of autonomous vehicles are described. Perception systems, focusing on visual sensors, the decision module and the prototyping, testing and evaluation of ADAS systems are all presented for effective implementation on autonomous and connected vehicles. This book also addresses cooperative systems, such as pedestrian detection, as well as the legal issues in the use of autonomous vehicles in open environments.
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.
Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS
This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.