  While artificial-intelligence-based methods suffer from lack of transparency,
rule-based methods dominate in safety-critical systems. Yet, the latter cannot
compete with the first ones in robustness to multiple requirements, for
instance, simultaneously addressing safety, comfort, and efficiency. Hence, to
benefit from both methods they must be joined in a single system. This paper
proposes a decision making and control framework, which profits from advantages
of both the rule- and machine-learning-based techniques while compensating for
their disadvantages. The proposed method embodies two controllers operating in
parallel, called Safety and Learned. A rule-based switching logic selects one
of the actions transmitted from both controllers. The Safety controller is
prioritized every time, when the Learned one does not meet the safety
constraint, and also directly participates in the safe Learned controller
training. Decision making and control in autonomous driving is chosen as the
system case study, where an autonomous vehicle learns a multi-task policy to
safely cross an unprotected intersection. Multiple requirements (i.e., safety,
efficiency, and comfort) are set for vehicle operation. A numerical simulation
is performed for the proposed framework validation, where its ability to
satisfy the requirements and robustness to changing environment is successfully
demonstrated.
