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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.
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.
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.
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.
This fundamental work explains in detail systems for active safety and driver assistance, considering both their structure and their function. These include the well-known standard systems such as Anti-lock braking system (ABS), Electronic Stability Control (ESC) or Adaptive Cruise Control (ACC). But it includes also new systems for protecting collisions protection, for changing the lane, or for convenient parking. The book aims at giving a complete picture focusing on the entire system. First, it describes the components which are necessary for assistance systems, such as sensors, actuators, mechatronic subsystems, and control elements. Then, it explains key features for the user-friendly design of human-machine interfaces between driver and assistance system. Finally, important characteristic features of driver assistance systems for particular vehicles are presented: Systems for commercial vehicles and motorcycles.
This book proposes new technologies and discusses future solutions for ICT design infrastructures, as reflected in high-quality papers presented at the 5th International Conference on ICT for Sustainable Development (ICT4SD 2020), held in Goa, India, on 23–24 July 2020. The conference provided a valuable forum for cutting-edge research discussions among pioneering researchers, scientists, industrial engineers, and students from all around the world. Bringing together experts from different countries, the book explores a range of central issues from an international perspective.
Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle. Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology. - Provides a comprehensive introduction to the technology stack of a self-driving vehicle - Covers the three domains of perception, planning and control - Offers foundational theory and best practices - Introduces advanced control algorithms and high-potential areas of new research - Gives engineers, technicians and students an accessible handbook to self-driving vehicle technology and applications
This book is the second volume reflecting the shift in the design paradigm in automobile industry. It presents contributions to the second and third workshop on Automotive Systems Engineering held in March 2013 and Sept. 2014, respectively. It describes major innovations in the field of driver assistance systems and automated vehicles as well as fundamental changes in the architecture of the vehicles.
The book expounds the current research and development trend of intelligent safety technology of automobile, and analyzes and excavates the new safety technology to the automobile. It introduces the basic theory, core method, key technology, main system, test evaluation and innovation practice of intelligent safety of automobile for readers, providing a certain theoretical and practical basis for the safety development of the automobile.This book is elaborated from the perspective of the driver-vehicle-road system. The traffic accidents are divided into three stages for discussion: before, during and after the collision. This book constructs a new systematic structure for Safety theory and technical system of several key links, including system safety, operation safety, intelligent protection and safety evaluation. It will be a useful reference for researchers and practitioners in the field of automobile engineering and auto pilot.