Active collision avoidance system plays a crucial role in ensuring the lateral safety of autonomous vehicles, and it is primarily related to path planning and tracking control algorithms. In particular, the direct yaw-moment control (DYC) system can significantly improve the lateral stability of a vehicle in environments with sudden changes in road conditions. In order to apply the DYC algorithm, it is very important to accurately consider the properties of tire forces with complex nonlinearity for control to ensure the lateral stability of the vehicle. In this study, longitudinal and lateral tire forces for safety path tracking were simultaneously estimated using a long short-term memory (LSTM) neural network based estimator. Furthermore, to improve path tracking performance in case of sudden changes in road conditions, a system has been developed by combining 4-wheel independent steering (4WIS) model predictive control (MPC) and 4-wheel independent drive (4WID) direct yaw-moment control (DYC). The estimation performance of the extended Kalman filter (EKF), which are commonly used for tire force estimation, was compared. In addition, the estimated longitudinal and lateral tire forces of each wheel were applied to the proposed system, and system verification was performed through simulation using a vehicle dynamics simulator. Consequently, the proposed method, the integrated path tracking algorithm with DYC and MPC using the LSTM based estimator, was validated to significantly improve the vehicle stability in suddenly changing road conditions.

翻译：主动防碰撞系统在确保自动驾驶车辆侧向安全中起关键作用，主要涉及路径规划与跟踪控制算法。特别是直接横摆力矩控制（DYC）系统能在道路条件突变环境下显著提升车辆侧向稳定性。为应用DYC算法，精确考虑具有复杂非线性的轮胎力特性对保障车辆侧向稳定性至关重要。本研究采用基于长短期记忆（LSTM）神经网络的估计器，同步估计安全路径跟踪所需的纵向与侧向轮胎力。此外，为提升道路条件突变时的路径跟踪性能，开发了融合四轮独立转向（4WIS）模型预测控制（MPC）与四轮独立驱动（4WID）直接横摆力矩控制（DYC）的集成系统。与常用于轮胎力估计的扩展卡尔曼滤波（EKF）的估计性能进行了对比，并将各车轮的估计纵横向轮胎力应用于所提系统，通过车辆动力学仿真器进行系统验证。结果表明，所提基于LSTM估计器的DYC与MPC集成路径跟踪算法，在道路条件突变时能显著提升车辆稳定性。