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Manufacturers of safety-critical systems must make the case that theirproduct is sufficiently safe for public deployment. Much of this case oftenrelies upon critical event outcomes from real-world testing, requiringmanufacturers to be strategic about how they allocate testing resources inorder to maximize their chances of demonstrating system safety. This workframes the partially observable and belief-dependent problem of test schedulingas a Markov decision process, which can be solved efficiently to yieldclosed-loop manufacturer testing policies. By solving for policies over a widerange of problem formulations, we are able to provide high-level guidance formanufacturers and regulators on issues relating to the testing ofsafety-critical systems. This guidance spans an array of topics, includingcircumstances under which manufacturers should continue testing despiteobserved incidents, when manufacturers should test aggressively, and whenregulators should increase or reduce the real-world testing requirements for anautonomous vehicle.

Closed-Loop Policies for Operational Tests of Safety-Critical Systems