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Automotive Engineering ›› 2023, Vol. 45 ›› Issue (12): 2222-2233.doi: 10.19562/j.chinasae.qcgc.2023.12.004

Special Issue: 智能网联汽车技术专题-控制2023年

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Human-Vehicle Shared Steering Control System for Dense Obstacle Avoidance

Liang Yan1,Xiaodong Wu2(),Chuan Hu2   

  1. 1.School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai  200240
    2.Shanghai Jiao Tong University,National Engineer Research Center of Automotive Power and Intelligent Control,Shanghai  200240
  • Received:2023-05-31 Revised:2023-06-29 Online:2023-12-25 Published:2023-12-21
  • Contact: Xiaodong Wu E-mail:xiaodongwu@sjtu.edu.cn

Abstract:

The innovation and development of vehicle chassis technology have injected new vitality into the design of intelligent driving systems. Combining the physical decoupling characteristics of steer-by-wire (SBW) systems, a human-machine shared steering control system for dense obstacle avoidance scenarios is proposed. Firstly, based on vehicle dynamics and the Monte Carlo tree search (MCTS) algorithm, a receding horizon steering field histogram (RH-SFH) method is utilized to achieve the real-time obstacle avoidance decision-making and planning. Secondly, a short-term driver behavior prediction model based on gated recurrent unit (GRU) network is established, which can directly output temporal signal of steering command. Then, expected time-to-collision is calculated referring to the vehicle steering characteristics, and the risk evaluation model is established accordingly. Finally, a dynamic allocation strategy for vehicle steering control rights is constructed, and hardware-in-the-loop experiments are conducted in the joint simulation environment based on MATLAB/Simulink. The results show that the shared steering control method can effectively reduce collision, improve the driving safety, and reduce the operational burden of drivers while ensuring the traffic efficiency compared with manual driving under multiple working conditions.

Key words: indirect shared control, human-machine coordination, collision avoidance planning, driver behavior prediction