Survey of Predictive Cruise Control for Vehicle Platooning

doi:10.3901/JME.2024.18.218

Abstract

Abstract: Vehicle platoon cruise control is mainly based on local limited traffic environment information. However, high uncertainty of the environment can affect vehicle modeling accuracy and control performance. As an evolution of cruise control, predictive cruise control has become a current research hotspot. In order to comprehensively analyze the research progress of vehicle platoon predictive cruise control, there are summarized four aspects, i.e., traffic environment information prediction, platoon motion behavior decision-making, vehicle trajectory planning, and vehicle trajectory tracking control. Firstly, it is introduced the research progress of vehicle platoon prediction of traffic environment information, including geography and traffic information of its road ahead through vehicle-infrastructure cooperation, and predicting motion states of surrounding vehicles through on-board sensors. The status quo and its trends of trajectory prediction methods based on deep learning are mainly introduced; Secondly, it is introduced the progress of decision-making of cooperative vehicle motion behaviors, while emphatically introducing the important roles of game theory and machine learning in this field, and pointing out trends of motion behaviors decision-making using the combined optimization with physical model and data; Thirdly, aiming at the problem of vehicle cooperative trajectory planning, current researches are sorted out from the perspectives of model-driven and data-driven, while advantages of reinforcement learning in collaborative trajectory planning are illustrated; Then, the problem of vehicle trajectory tracking control is expounded from two aspects of predictive cruise control and vehicle tracking control, respectively, while it is pointed out that the vehicle control method jointly driven by model and data has great application potential; Finally, the status quo and shortcomings of vehicle platoon predictive cruise control are summarized, and future trends in this field are prospected to provide new ideas for its application.

Key words: autonomous driving, vehicle platooning, predictive cruise control, machine learning, jointly driven by model and data

CLC Number:

U469

CHU Duanfeng, LIU Hongxiang, GAO Bolin, WANG Jinxiang, YIN Guodong. Survey of Predictive Cruise Control for Vehicle Platooning[J]. Journal of Mechanical Engineering, 2024, 60(18): 218-246.

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