Abstract In the development of autonomous driving technology, safety is always regarded as a top priority. The behavior decision-making and motion planning systems, as key components of the technology, possess higher requirements for intelligence. They need continuously make optimal strategies and behaviors according to the changing environment to ensure the safety of vehicle driving. The behavior decision-making and motion planning systems are expounded. Firstly, the theory and applications of rule-based decision algorithms, supervised learning-based decision algorithms, and reinforcement learning-based decision algorithms are introduced. Then, sampling-based planning algorithms, graph search-based planning algorithms, numerical optimization-based planning algorithms and interaction-based planning algorithms in motion planning are discussed and their designs are discussed. Behavior decision-making and motion planning are analyzed from the perspective of safety, and the advantages and disadvantages of various methods are compared. Finally, future research directions and challenges for safety in the field of autonomous driving are predicted.
Fund:National Key R&D Program of China(No.2021ZD0110705)
Corresponding Authors:LIU jianyi, Ph.D., associate professor. His research interests include face recognition and robot navigation.
About author:: GUAN Xin, master student. His research interests include motion planning for autonomous vehicles.SHI Jiamin, Ph.D. candidate. Her research interests include autonomous driving based on reinforcement learning.CHEN Shitao, Ph.D, assistant lecturer. His research interests include autonomous dri-ving.ZHENG Nanning, Ph.D.,professor. His research interests include computer vision and pattern recognition.
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