Research on vehicle lateral stability control under low-adhesion road conditions using proximal policy optimization algorithm

Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. To address the issue of maintaining vehicle stability during emergency braking on roads with low and non-uniform adhesion, this paper proposes an intelligent integrated longitudinal and lateral stability control algorithm based on the Proximal Policy Optimization (PPO) algorithm. Firstly, high-fidelity models of electromechanical braking (EMB) and steer-by-wire (SBW) systems are constructed in Amesim by leveraging their dynamic characteristics, while a full-vehicle dynamics model is developed in CarSim. The dynamic accuracy of the drive-by-wire system is verified through input-output tracking analysis. Next, vehicle stability is analyzed using vehicle dynamics models to optimize reinforcement learning control variables. This involves designing a continuous state space and action space that incorporate vehicle states and road surface parameters. A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. Training is conducted via an Amesim-CarSim-Python co-simulation platform for emergency braking scenarios on split-μ roads, low-adhesion surfaces, and curved roads. Results show that, compared to Model Predictive Control (MPC) and Sliding Mode Control (SMC), the PPO algorithm reduces braking distance by 15-20% on low-adhesion roads, decreases lateral deviation by 25-30% on split-μ roads, and suppresses yaw rate oscillation by 28.8% on curved roads. Hardware-in-the-loop (HIL) validation confirms the algorithm's robustness under extreme conditions, with lateral stability metrics maintained within safety thresholds.