  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.
