Constraint optimization and key factor analysis based vehicle emergency braking strategy generator

Enhancing vehicle emergency braking performance is crucial for vehicular safety and reliability. We have observed that the traditional vehicle dynamics based model predictive control (MPC) algorithm which is used to produce emergency braking strategy fails to achieve the minimization of emergency braking distance. To address this issue, this article employed a simulation based approach to improve emergency braking performance via machine learning based algorithms. We design a data optimization model which optimizes the longitudinal forces and slip ratios of the four wheels based on back-propagation neural network (BPNN) and constraint optimization algorithm to reduce emergency braking distance. In addition, a Balltree nearest neighbor search based generator is proposed to produce superior emergency braking strategies for real-time purpose. The experimental results demonstrate that the emergency braking strategy which we optimized achieves an average enhancement of 13% in emergency braking performance compared to the traditional MPC algorithm. Unlike other machine learning based algorithms that have a delay of over 0.01 s, the proposed generator has an execution time of 0.0008 s, meeting the delay requirements under emergency braking conditions.