In the field of autonomous driving, simulation-based testing is an important means for identifying and addressing long-tail problems. This paper proposes a driving risk field model that considers road curvature radius for cut-in scenarios at curves. By integrating Prescan/Simulink simulation platform and genetic algorithms, an automated risk scenario generation framework is constructed. The framework uses the relative driving safety index (RDSI) as the optimization objective, which overcomes the limitations of traditional risk indicators that fail to promptly recognize cut-in risks by preceding vehicles in complex scenarios. Various cutin scenarios under different road curvature radii are generated through simulation, and typical test cases are selected for analysis. The results show that the RDSI indicator achieves a 65.6% higher warning success rate compared to the time-to-collision (TTC) indicator and can identify potential risks earlier. Additionally, experiments reveal that different road curvature radii significantly impact collision risk.