AdvDiffuser: 誘導拡散による敵対的危険運転シナリオの生成
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion
拡散モデルと軽量ガイドモデルを組み合わせ、自然な周囲車両の挙動を保ちながら様々な自動運転システムに転用可能な危険シナリオを生成する手法を提案。
著者: Yuting Xie, Xianda Guo, Cong Wang, Kunhua Liu, Long Chen
分類: cs.LG, cs.RO
原文アブストラクト
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.
