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.

翻译：自动驾驶车辆（AVs）有望彻底改变交通运输行业，但必须经过严格测试以避免安全隐患。仿真测试在发现自动驾驶系统（ADSs）的安全违规行为中起着关键作用。本文提出PAFOT，一种基于位置的测试框架，通过生成对抗性驾驶场景来暴露ADSs的安全漏洞。我们引入了一个围绕自车（EV）虚拟绘制的9位置网格，并修改非玩家角色（NPCs）的驾驶行为，使其在该网格内移动。PAFOT采用单目标遗传算法搜索对抗性测试场景。我们在著名的CARLA高保真模拟器上对PAFOT进行了验证。实验结果表明，PAFOT能够有效生成安全关键场景以破坏ADSs，并在较短时间内发现碰撞事件。此外，在相同驾驶条件下，PAFOT用更少的有效仿真时间发现了更多安全关键场景，优于其他基于搜索的测试技术。