Generation of Ego-Liable Hazardous-Test-Cases for Validating Automated Driving Systems in Junction-Scenes

During the transition from human-driving to autonomous-driving, Automated Driving Systems (ADS) have to deal with the crises caused by uncertain human-driving factors, especially in the junction-scenes which are flexible and complex. To validate the Safety of the Intended Functionality (SOTIF) in ADS with scenario-based tests, it is challenging to create reasonable hazardous-test-cases, which consider causing collision and avoiding liability simultaneously. Currently, parameter-search-based approaches with parameterized-maneuvers are widely utilized to explore test cases. However, in complicated scenes (i.e., junction-scenes), the trade-off between efficiency and fineness of maneuver-parameterization reduces the searching ability. To address the above challenges, we propose a cost-based controller with a designed state-transfer for collision to lead the agent vehicle (i.e., the vehicle causing events). The approach directly outputs continuous actions as the substitute for discrete parameterized-maneuvers, reducing the searching-state-space and enabling more detailed behaviors. In our real-time test, given different scenes in which Ego owns the right-of-way, our approach generates Ego-liable collision-cases with the success rates above 90%, higher than the ones using parameter-search-based method which are below 70%. More importantly, our method creates more hazardous cases with higher efficiency, which achieves 1.9-2.6 times of the impact-speed and takes only 18%-24% of time-consumption by contrast.