  We present a new approach to automated scenario-based testing of the safety
of autonomous vehicles, especially those using advanced artificial
intelligence-based components, spanning both simulation-based evaluation as
well as testing in the real world. Our approach is based on formal methods,
combining formal specification of scenarios and safety properties, algorithmic
test case generation using formal simulation, test case selection for track
testing, executing test cases on the track, and analyzing the resulting data.
Experiments with a real autonomous vehicle at an industrial testing facility
support our hypotheses that (i) formal simulation can be effective at
identifying test cases to run on the track, and (ii) the gap between simulated
and real worlds can be systematically evaluated and bridged.
