PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles
<|reference_start|>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.<|reference_end|>
@article{crespo-rodriguez2024pafot:,
    title={PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous
  Vehicles},
    author={Victor Crespo-Rodriguez, Neelofar, Aldeida Aleti},
    journal={arXiv preprint arXiv:2405.03326},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03326},
    primaryClass={cs.SE}
}