Rhodes, Greece. September 2-8, 2023.
Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization
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.