Research on the High-Speed Collision Avoidance Method of Distributed Drive Electric Vehicles

Abstract

High-speed autonomous emergency collision avoidance for intelligent vehicles is a challenging task. Many existing studies have not considered the effect of the road adhesion coefficient in the vehicle high-speed collision avoidance planning layer. Additionally, in lateral control, the effect of strong coupling of vehicle motion on the system model is not considered. This effect increases the uncertainty in the collision avoidance process and correlates with driving safety. In this regard, this paper conducted relevant studies. First, a distributed drive electric vehicle (DDEV) with controlled longitudinal speed was selected as the research platform and derived the transformed square-root cubature Kalman filter (T-SCKF) algorithm with cubature point sampling rule transformation to estimate the road adhesion coefficient. Second, the road adhesion coefficient information was used to calibrate the following vehicle safety distance model and the lane change collision avoidance lateral acceleration information was used to determine the vehicle autonomous emergency collision avoidance trajectory and to optimize the planning decision of the vehicle collision avoidance process. Last, the DDEV was laterally controlled to complete the tracking of the high-speed emergency collision avoidance trajectory by the adaptive model predictive control (Adaptive MPC) method with real-time updating of the system model. The relevant validation shows that the T-SCKF algorithm proposed in this paper can effectively estimate the road adhesion coefficient. The tracking control of the high-speed collision avoidance trajectory by the adaptive MPC method has better tracking accuracy and stability than traditional MPC and linear quadratic regulator (LQR) methods.