A Safety-Critical Decision-Making and Control Framework Combining Machine-Learning-Based and Rule-Based Algorithms

While machine-learning-based methods suffer from a lack of transparency, rule-based (RB) methods dominate safety-critical systems. Yet the RB approaches cannot compete with the first ones in robustness to multiple system requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, this article proposes a decision-making and control framework which profits from the advantages of both the RB and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. An RB switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized whenever the Learned one does not meet the safety constraint, and also directly participates in the Learned controller training. Decision-making and control in autonomous driving are chosen as the system case study, where an autonomous vehicle (AV) learns a multitask policy to safely execute an unprotected left turn. Multiple requirements (i.e., safety, efficiency, and comfort) are set to vehicle motion. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environments is successfully demonstrated.