Path Control for Post-Impact Vehicle Based on BPNN-PID Algorithm

Lateral displacements caused by vehicle collisions makes the vehicle travel unstable, which causes most of the accidents and even leads to even more collisions. In this paper, we will discuss the possibility of a control strategy that maintains the stability of the vehicle's travel after an impact and restricts the vehicle's in-lane travel, aiming to reduce the risk of subsequent accidents. Based on PID control, the post-collision control system controls the lateral deviation of the vehicle's travel path to the desired path by using an active steering system to ensure the trajectory and stability of the vehicle. In this paper, the PID control system parameters are combined with BP neural network to make timely, optimized adjustments to make the vehicle trajectory under the BP neural network PID controller converge faster and oscillate with less amplitude. Moreover, the BP neural network PID control system can automatically adjust the PID control parameters according to different collision situations. The effectiveness and adaptability of the BP neural network PID control system after collision is demonstrated through the simulation of post-collision steering control of the controlled vehicle.