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Journal of Automotive Safety and Energy ›› 2023, Vol. 14 ›› Issue (5): 580-590.DOI: 10.3969/j.issn.1674-8484.2023.05.007

• Intelligent Driving and Intelligent Transportation • Previous Articles     Next Articles

High real-time predictive control for active collision avoidance of intelligent vehicles

DUAN Jingliang1(), CHEN Liangfa1, WANG Wenxuan2, JIAO Chunxuan1, LIU Zhengyu2, MA Fei1, LI Shengbo2,*()   

  1. 1. University of Science and Technology Beijing, Beijing 100083, China
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
  • Received:2023-03-20 Revised:2023-07-13 Online:2023-10-31 Published:2023-10-30

Abstract:

A recurrent model predictive control (RMPC) algorithm was adopted to meet the high real-time requirements for active collision avoidance control in complex traffic scenarios. RMPC transformed the online optimization problem into an offline solution of recurrent policy parameters, and simulation experiments were conducted to validate its effectiveness. By introducing a penalty function, the constrained active collision avoidance optimization control problem was formulated as an unconstrained finite-time optimal control problem. The optimal solution of the control problem with different prediction steps was represented by a recurrent neural network. The learned policy to the prototype controller was deployed and its collision avoidance performance and online computational efficiency was verified by using CarSim. The results shows that the minimum vehicle distance during the collision process increases from 0.34 m to 1.38 m with the prediction step increasing from 12 to 20, and the number of collisions in a thousand experiments decreases from 44 to 0. Furthermore, the algorithm demonstrates a computational efficiency improvement of over 5.6 times compared to commonly used online optimization solvers when the prediction step is 15.

Key words: intelligent vehicle, model predictive control, recurrent function, lateral active collision avoidance

CLC Number: