Design of learning-based control with guarantees for autonomous vehicles in intersections

Abstract

This paper proposes a design method for the coordination of velocity profiles of autonomous vehicles in non-signalized intersection scenarios. The coordination is motivated by the avoidance of vehicle collision and the minimization of their energy loss resulted by stop and go maneuvers in the intersection. Therefore, the coordinated design is formed as an optimal control problem, which is solved through two optimization tasks. A quadratic optimization task with online solution is formed, which provides guarantees on the avoidance of the collision. Moreover, a reinforcement-learning-based optimization task with offline solution is formed, which is able to improve the economy performances of the autonomous vehicles. The optimization tasks are interconnected, i.e. the quadratic optimization with the vehicle model is used as an environment during the training process. The effectiveness of the proposed coordinated control through simulation examples with three number of autonomous vehicles is illustrated.