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

• URL: http://arxiv.org/abs/2405.03326v1
• Date: Mon, 6 May 2024 10:04:40 GMT
• Title: PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles
• Authors: Victor Crespo-Rodriguez, Neelofar, Aldeida Aleti,
• Abstract summary: This paper proposes PAFOT, a position-based approach testing framework. PAFOT generates adversarial driving scenarios to expose safety violations of Automated Driving Systems. Experiments show PAFOT can effectively generate safety-critical scenarios to crash ADSs and is able to find collisions in a short simulation time.
• Score: 4.243926243206826
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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