Safe Scenario Parameter Region Synthesis for End-to-End Autonomous Driving

End-to-end autonomous driving models have demonstrated great potential in driving safely under normal conditions. Existing techniques that evaluate their behavior on long-tail scenarios focus on generating diverse scenarios to test the driving system. In this work, we characterize safety-critical scenarios by certain parameters, and study the synthesis of the set of values for these parameters under which the Autonomous Vehicle is safe. We formalize the problem of synthesizing safe and maximal scenario parameter regions, and propose a framework to solve it. Our framework, Odin, uses a counterexample-driven algorithm to achieve its objective. We evaluate Odin on safety-critical scenarios for its efficacy in synthesizing safe regions and producing safe/unsafe pairs of parameters using the regions.