The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above.

The 13 papers in the volume focus on driver performance and simulation. Human engineering of advanced technology to aid drivers is discussed first: topics include the design of auditory interfaces new to the world of automobiles and the design of vehicle controls and models of human performance to use in evaluating the development of high technology for drivers. Then human behavior in the driving environment is explored in studies of eye scanning and driver work load. Ways to improve information to the driver are examined from the perspective of the vehicle, through the use of daytime running lights, and from sources external to the vehicle, such as broadcasting, signs, and markings. The Record concludes with papers on simulation of vehicle dynamics and motion.

Automated Vehicle (AV) technology has been developed to reduce injuries, improve mobility, and release drivers from driving tasks. However, many expected benefits of AVs may not be fully achieved without full market penetration of AVs, but it will be impossible to reach a 100% penetration rate in the near future, and there will be a stage of a mixed transportation system in which human-driven vehicles (HVs) share the roads with AVs. Therefore, it is necessary to investigate drivers' subjective feelings, decision-making, and driver performance in inter-vehicle (HV-AV) interactions. In this thesis, one objective is to investigate the effects of drivers' driving styles, types of interaction, and AV penetration rates on drivers' subjective feelings, decision-making, and driving performance when driving HVs in mixed traffic. A supervised web-based experiment was conducted to explore drivers' subjective feelings and decision-making in mixed traffic and found that the drivers' driving styles and the types of interaction significantly influenced their subjective feelings and decision-making. Based on the results, a formal laboratory study was conducted to investigate the impact of the driver's driving styles and the AV penetration rates on their subjective feelings, decision-making, and driving performance. The results revealed that drivers' driving styles and AV penetration rates significantly influenced their subjective feelings, decision-making, and driving performance. A machine learning model was developed based on the architecture of the maximum entropy inverse reinforcement learning (IRL) framework to predict HV drivers' decision-making and driving performance in mixed traffic under different AV penetration rates, types of interactions, and drivers' driving styles. These works provide insights into the understanding of human drivers' decision-making processes and responses to HV-AV interactions in mixed traffic. The application of this model could be used to enhance the design of automated systems based on the predictions of other drivers to assist AVs in better negotiating mixed traffic, avoiding traffic conflicts, and improving road safety.

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 presents a general overview of the various factors that contribute to modelling human behaviour in automotive environments. This long-awaited volume, written by world experts in the field, presents state-of-the-art research and case studies. It will be invaluable reading for professional practitioners graduate students, researchers and alike.