Research on the Motion Planning Strategy for Autonomous Vehicles in Extreme Conditions

doi:10.3901/JME.2022.22.349

Abstract

Abstract: The research content is the motion planning of autonomous vehicles in extreme conditions, and a motion planning strategy for autonomous vehicles adapting to extreme conditions is proposed. Firstly, a dynamic model which can accurately describe the motion of vehicle is established, and a modified nonlinear tire model is used to reflect the mechanical properties between the tire and road surfaces. Secondly, an adaptive potential field model based on safe braking distance is proposed to adapt the change of external conditions and vehicle parameters in extreme conditions. Furthermore, considering the vehicle prone to lateral instability under extreme conditions, the lateral stability index(LSI) is designed as the key optimization parameter, and the lateral stability of vehicle is analyzed. Then, based on model predictive control(MPC) method, the motion planning problem in the extreme conditions is transformed into a multi-objective optimization problem. Finally, the PreScan-Simulink-CarSim co-simulation platform is built, and the proposed motion planning strategy is verified in various extreme conditions, such as snow and ice covered road. The simulation results show that the strategy effectively improves the safety and stability of the autonomous vehicle in the extreme conditions.

Key words: autonomous vehicles, motion planning, extreme conditions, lateral stability, adaptive potential field

CLC Number:

TG156

YANG Xin, TANG Xiao-lin, YANG Kai, XU Zheng-ping, HU Xiao-song. Research on the Motion Planning Strategy for Autonomous Vehicles in Extreme Conditions[J]. Journal of Mechanical Engineering, 2022, 58(22): 349-359.

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