Journal
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
Volume 3, Issue 3, Pages 317-328Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIV.2018.2843128
Keywords
Decision making; system testing; safety management; autonomous vehicles
Categories
Funding
- National Science Foundation Graduate Research Fellowship Program [DGE114747]
- Robert Bosch LLC.
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Manufacturers of safety-critical systems must make the case that their product is sufficiently safe for public deployment. Much of this case often relies upon critical event outcomes from real-world testing, requiring manufacturers to be strategic about how they allocate testing resources in order to maximize their chances of demonstrating system safety. This paper frames the partially observable and the belief-dependent problem of test scheduling as a Markov decision process, which can be solved efficiently to yield closed-loop manufacturer testing policies. By solving for policies over a wide range of problem formulations, we are able to provide high-level guidance for manufacturers and regulators on issues relating to the testing of safety-critical systems. This guidance spans an array of topics, including circumstances under which manufacturers should continue testing despite observed incidents, when manufacturers should test aggressively, and when regulators should increase or reduce the real-world testing requirements for safety-critical systems.
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