In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies.

In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

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Jie Ji is currently an Associate Professor with the College of Engineering and Technology, Southwest University, Chongqing, China. He received a Ph.D. in Mechanical Engineering from Chongqing University in 2010. From December 2013 to December 2014, he was a Postdoctoral Fellow withthe Department of Mechanical and Mechatronics Engineering, University of Waterloo, Ontario, Canada. His active research interests include advanced control & artificial intelligence, and their applications on intelligent and connected vehicles, in which he has contributed more than 30 articles and obtained 9 granted China patents. He is the recipient of the Best Land Transportation Paper Award from IEEE Vehicular Technology Society in 2019, and the Young Professional Excellent Paper Award from China SAE in 2019. Dr. Ji is a member of the China Society of Automotive Engineers, and is the founder of the Green Intelligent Vehicle and Electromobile Laboratory (GIVE Lab) at Southwest University (from 2015).HongWang is currently a Research Associate Professor at Tsinghua University. From the year 2015-2019, she was working as a Research Associate of Mechanical and Mechatronics Engineering with the University of Waterloo. She received her Ph.D. in Beijing Institute of Technology in China in 2015. Her research focuses on the risk assessment and crash mitigation-based decision making during critical driving scenarios, ethical decision making for autonomous vehicles, component sizing, modelling of hybrid powertrains and intelligent control strategies design for hybrid electric vehicles, and intelligent control theory and application. She becomes the IEEE member since the year 2017. She has published over 50 papers in top international journals, such as IEEE Transaction on Intelligent System, IEEE Transaction on Vehicular Technology, etc.Yue Ren received a B.E. and a Ph.D. in Mechanical and Mechatronics Engineering from Chongqing University, China, in 2013 and 2018, respectively. He iscurrently working as an assistant professor with the college of engineering and technology, Southwest University, China. He is working on autonomous vehicles, including vehicle detection, path planning and tracking, vehicle dynamics, and stability control.