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

TL;DR

This paper presents an integrated path tracking system for autonomous vehicles that combines LSTM-based tire force estimation with DYC and MPC control strategies to enhance stability during sudden road condition changes.

Contribution

It introduces a novel LSTM neural network for real-time tire force estimation integrated with DYC and MPC for improved vehicle stability in dynamic environments.

Findings

LSTM estimator outperforms EKF in tire force accuracy

Integrated control improves vehicle stability in simulation

System effectively adapts to sudden road condition changes

Abstract

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.