  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.
