PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles

Autonomous Vehicles (AVs) are prone to revolutionise the transportation industry.However, they must be thoroughly tested to avoid safety violations.Simulation testing plays a crucial role in finding safety violations of Automated Driving Systems (ADSs).This paper proposes PAFOT, a position-based approach testing framework, which generates adversarial driving scenarios to expose safety violations of ADSs.We introduce a 9-position grid which is virtually drawn around the Ego Vehicle (EV) and modify the driving behaviours of Non-Playable Characters (NPCs) to move within this grid.PAFOT utilises a single-objective genetic algorithm to search for adversarial test scenarios.We demonstrate PAFOT on a well-known high-fidelity simulator, CARLA.The experimental results show that PAFOT can effectively generate safety-critical scenarios to crash ADSs and is able to find collisions in a short simulation time.Furthermore, it outperforms other search-based testing techniques by finding more safety-critical scenarios under the same driving conditions within less effective simulation time.