A²CoST: an ASP-based Avoidable Collision Scenario Testbench for Autonomous Vehicles.

This paper addresses the challenge of generating safety-critical scenarios with multiple adversarial vehicles for testing autonomous vehicles. Such scenarios must be plausible and collision-avoidable while resulting in a collision with the vehicle-under-test. However, the tremendous number of scenarios and the low ratio of plausible scenarios makes previous methods squander primary resources on implausible scenarios, degenerating their efficiency. We propose a two-stage framework called the ASP-based Avoidable Collision Scenario Testbench (A²CoST) to overcome this obstacle and improve efficiency. In the former stage, we apply Answer Set Programming (ASP) for generating plausible logical scenarios. In the latter stage, we use a search algorithm to refine logical scenarios into safety-critical concrete scenarios. We also compute collision-free trajectories in these concrete scenarios while the vehicle-under-test fails to avoid the collision. We empirically show the A²CoST significantly decreases the time consumption for simple scenarios while still effectively generating complex critical scenarios. The comparison with real-world traffic data further demonstrates the value of A²CoST in generating plausible scenarios. The source codes of our method and the baselines are opened at https://github.com/Autonomous-Driving-Safety-Project/AACoST.