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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.

危险工况下的车辆横向稳定性控制，是智能驾驶主动安全领域的核心挑战之一。针对低附着、不均附着路面紧急制动场景下的车辆稳定性维持难题，本文提出一种基于近端策略优化（Proximal Policy Optimization，PPO）算法的智能纵横向集成稳定性控制算法。首先，基于机电制动（Electromechanical Braking，EMB）与线控转向（Steer-by-Wire，SBW）系统的动态特性，在Amesim中构建其高保真模型，并在CarSim中搭建整车动力学模型。通过输入输出跟踪分析，验证了线控系统的动态精度。随后，借助车辆动力学模型开展车辆稳定性分析，以优化强化学习控制变量：设计融合车辆状态与路面参数的连续状态空间与动作空间。基于轮胎侧偏角、车身侧偏角与横摆角速度的临界阈值等稳定性指标，构建多目标奖励函数。基于Amesim-CarSim-Python联合仿真平台，针对附着系数分离路面、低附着路面与弯道场景下的紧急制动工况开展训练。结果表明，相较于模型预测控制（Model Predictive Control，MPC）与滑模控制（Sliding Mode Control，SMC），PPO算法在低附着路面可缩短制动距离15%~20%，在附着系数分离路面可降低横向偏差25%~30%，在弯道场景可抑制横摆角速度振荡28.8%。硬件在环（Hardware-in-the-loop，HIL）验证结果表明，该算法在极端工况下具备鲁棒性，车辆横向稳定性指标均维持在安全阈值范围内。