Performance Guarantees on Machine-Learning-based Overtaking Strategies for Autonomous Vehicles

The control of autonomous vehicles in overtaking scenarios is an important challenge, in which an autonomous vehicle in a multiple vehicle environment must be safely driven. Due to the complexity of vehicle scenarios, several machine-learning-based design strategies have been developed, which provide outstanding results. However, in most of these methods it is difficult to provide a theoretical guarantee on the most important performance of the overtaking strategy, i.e., the avoidance of collisions with the surrounding vehicles. This paper proposes a design architecture with which this performance can be guaranteed. The method is based on the robust control framework and it is independent from the structure of the machine-learning-based agent. The effectiveness of the method is illustrated through simulation examples.