AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
This addresses the need for transferable and realistic adversarial scenarios in autonomous driving testing, though it is incremental as it builds on existing diffusion and adversarial methods.
The paper tackles the problem of generating safety-critical driving scenarios for autonomous vehicle testing by proposing AdvDiffuser, a framework using guided diffusion models, which outperforms existing methods in realism, diversity, and adversarial performance on the nuScenes dataset.
Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.