A Path-Driven Probabilistic Framework for Simulating Abnormal Behavior in Autonomous Driving Scenarios

Achieving high-level autonomous driving poses significant challenges, primarily due to the extensive and time-consuming testing required for low-probability abnormal behavior scenarios. Existing simulation platforms are limited by microscopic traffic flow models based on car-following and lane-changing theories (focused on the behavior of individual vehi-cles), which limits the implementation of complex and diverse abnormal behaviors such as road deviation and cutting in. This paper proposes an Abnormal behavior Generation model based on Event Probability triggers and Static paths (AGEPS model), which is capable of continuously and automatically generating abnormal behaviors without disrupting normal traffic flow. The AGEPS model is computationally efficient and scalable, as it decouples path optimization and trajectory tracking from obstacle avoidance, using explicit control law for both tracking and avoidance. The paper demonstrates three typical abnormal behaviors: Overtaking on the Right (OOR), Driving on the Lane Line (DOL), and Sudden Braking (SUB), indicating that the AGEPS model effectively generates these behaviors by selecting and tracking target static paths while adjusting lateral offsets and desired speeds. Experimental results validate the AGEPS model's effectiveness and its ability to generate abnormal behaviors continuously. Simulation results indicate that the AGEPS model reduces single-step computation time (including decision-making and control) by over 92.8 % compared to the MPC controller, with a single-step execution time of just 4 ms.