Post-Collision Vehicle Trajectory Control Based on BPNN-PID Algorithm and Fuzzy PID Algorithm

In an increasingly evolving world of vehicle safety, there are still some situations where vehicle avoidance is not possible or is not done in time leading to vehicle collisions and thus accidents, and can even lead to multiple collisions. In my paper, it proposed a control strategy designed for stabilizing vehicle travel after a crash, thereby mitigating the chances of further accidents. The post-collision control system manages vehicle travel by adjusting steering and braking, effectively reducing speed to maintain trajectory and stability. Variations in the collision angle result in differing impacts on the vehicle, which is classified and discussed in this paper. This paper combines the parameters of control system based on PID with BP neural network to optimize and improve the parameters of PID immediately. This paper analyzes the control performance of fuzzy PID and BPNN-PID, demonstrating the effectiveness and adaptability of the BP neural network based on PID control system for post-collision stability control through simulations involving the affected vehicle. Meanwhile, the use of different control strategies in the same controller d0esign also makes the control effect of the vehicle different.