SAGA: Generating Extreme Scenarios for Autonomous Driving via Adversarial Perturbations

Yan Wang, Qiong Wu, Xiaoxu Shi, Yishan Li, Keqin Li

Abstract: Evaluating autonomous vehicles’ performance in complex, long-tail traffic scenarios, especially under extreme conditions, often highlights the limitations of existing methods in generating realistic and challenging scenarios, which can affect vehicle safety and reliability. To address these gaps, this paper proposes a Scenario-Adaptive Gradient Adjustment (SAGA) network, an adversarial model specifically designed to generate intricate traffic scenarios that closely mimic real-world dynamics. The SAGA network includes a generator and a victim model, where the generator uses adversarial sequences based on the kinematic bicycle model to simulate dynamic vehicle characteristics and calculate precise gradients. These gradients are then used to perturb the victim model, creating safety-critical scenarios essential for evaluating autonomous vehicle performance, such as emergency evasions and complex intersection navigation. Additionally, we use a k-means++ clustering method tailored to categorize ten types of safety-critical scenarios for autonomous driving. The generated scenarios are comprehensive, diverse, and challenging, providing a robust testing environment for autonomous vehicles. Simulation results demonstrate the SAGA network’s effectiveness, significantly outperforming traditional non-transparent optimization and the KING method. SAGA achieved a 25% higher success rate than non-transparent optimization and a 2% improvement over the KING method in generating complex scenarios, along with an 8% increase in interpretability, reaching 80%. These findings highlight the capability of SAGA-generated scenarios to thoroughly assess autonomous vehicle performance under diverse traffic conditions, ensuring safer operations.

External IDs:doi:10.1109/tits.2025.3640578