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Active collision avoidance system plays a crucial role in ensuring thelateral safety of autonomous vehicles, and it is primarily related to pathplanning and tracking control algorithms. In particular, the direct yaw-momentcontrol (DYC) system can significantly improve the lateral stability of avehicle in environments with sudden changes in road conditions. In order toapply the DYC algorithm, it is very important to accurately consider theproperties of tire forces with complex nonlinearity for control to ensure thelateral stability of the vehicle. In this study, longitudinal and lateral tireforces for safety path tracking were simultaneously estimated using a longshort-term memory (LSTM) neural network based estimator. Furthermore, toimprove 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) directyaw-moment control (DYC). The estimation performance of the extended Kalmanfilter (EKF), which are commonly used for tire force estimation, was compared.In addition, the estimated longitudinal and lateral tire forces of each wheelwere applied to the proposed system, and system verification was performedthrough simulation using a vehicle dynamics simulator. Consequently, theproposed method, the integrated path tracking algorithm with DYC and MPC usingthe LSTM based estimator, was validated to significantly improve the vehiclestability in suddenly changing road conditions.

Integrated Path Tracking with DYC and MPC using LSTM Based Tire Force Estimator for Four-wheel Independent Steering and Driving Vehicle