Generating risk scenarios adverse to cooperative vehicle-infrastructure perception: An optimization-based framework

Existing approaches for concrete scenario generation and assessment primarily focus on trajectory-level interactions while neglecting physical perception constraints, such as sensor field-of-view limitations and occlusions from surrounding objects, leading to unrealistic risk representations. To fill this gap, this study proposes a framework that integrates vehicle dynamics, environmental constraints, and perception limitations into a unified generation process. Specifically, vehicle trajectories are parameterized using a kinematic bicycle model and perturbed through controlled variations in longitudinal acceleration and steering angle. A voxel-based surrogate perception metric is introduced to efficiently evaluate cooperative perception (CoP) by modeling line-of-sight occlusions, which achieves a balance between computational efficiency and accuracy. By modeling perception limitations, the framework generates not only risky but also CoP-adverse scenarios. A multi-constraint optimization framework driven by the genetic algorithm is proposed, which ensures that generated scenarios satisfy physical feasibility, road compliance, collision avoidance, and degraded perception conditions, while increasing interaction risk between agents. Experimental results based on real-world trajectory datasets demonstrate that the proposed approach can effectively generate diverse and high-risk scenarios with reduced CoP performance and satisfactory computational efficiency. This framework may not only help generate CoP-adverse scenarios for the assessment and evaluation of vehicle-infrastructure CoP systems, but also offer a tool for the construction of scenario library to support end-to-end learning-based methods.