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While artificial-intelligence-based methods suffer from lack of transparency,rule-based methods dominate in safety-critical systems. Yet, the latter cannotcompete with the first ones in robustness to multiple requirements, forinstance, simultaneously addressing safety, comfort, and efficiency. Hence, tobenefit from both methods they must be joined in a single system. This paperproposes a decision making and control framework, which profits from advantagesof both the rule- and machine-learning-based techniques while compensating fortheir disadvantages. The proposed method embodies two controllers operating inparallel, called Safety and Learned. A rule-based switching logic selects oneof the actions transmitted from both controllers. The Safety controller isprioritized every time, when the Learned one does not meet the safetyconstraint, and also directly participates in the safe Learned controllertraining. Decision making and control in autonomous driving is chosen as thesystem case study, where an autonomous vehicle learns a multi-task policy tosafely cross an unprotected intersection. Multiple requirements (i.e., safety,efficiency, and comfort) are set for vehicle operation. A numerical simulationis performed for the proposed framework validation, where its ability tosatisfy the requirements and robustness to changing environment is successfullydemonstrated.

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