Adaptive MPC‐Based AEB Control Strategy with Dynamic Weight and Sampling Time Adjustment

Given that constant‐parameter model predictive control (MPC)‐based automatic emergency braking (AEB) systems are unable to simultaneously balance comfort, safety, and computational efficiency, this paper proposes an adaptive optimization strategy for MPC that adjusts weights and sampling times to address this issue. First, a three‐level warning strategy based on a safety distance model is introduced. This strategy assesses the risk level during vehicle operation using an emergency coefficient and adaptively adjusts the sampling time. By analysing the correlation between relative distance, speed, and injury risk, weight adjustment rules based on injury risk are determined. Second, a fuzzy regulator is developed with the emergency coefficient, injury risk, and relative distance as inputs is developed to enable dynamic adjustment of weights and sampling time in response to operational conditions. Finally, an AEB control strategy is designed based on hierarchical control principles: the upper layer uses MPC to achieve multi‐objective optimization, while the lower layer employs PID correction to track the desired acceleration. In the test scenarios, joint simulation experiments were conducted using CarSim and MATLAB/Simulink, and the results under four scenarios and operating conditions were analysed and compared. The results show that the proposed control strategy enhances comfort while ensuring AEB safety control, reducing the average braking distance deviation by 11.80% and the average acceleration deviation by 48.91%. These improvements are significant for enhancing AEB performance without hardware modifications.