Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

The paper proposes a novel learning-based coordination strategy for lateral control systems of automated vehicles. The motivation of the research is to improve the performance level of the coordinated system compared to the conventional model-based reconfigurable solutions. During vehicle maneuvers, the coordinated control system provides torque vectoring and front-wheel steering angle in order to guarantee the various lateral dynamical performances. The performance specifications are guaranteed on two levels, i.e., primary performances are guaranteed by Linear Parameter Varying (LPV) controllers, while secondary performances (e.g., economy and comfort) are maintained by a reinforcement-learning-based (RL) controller. The coordination of the control systems is carried out by a supervisor. The effectiveness of the proposed coordinated control system is illustrated through high velocity vehicle maneuvers.