DualShield: 到達可能性解析による安全なモデル予測拡散と対話型自動運転
DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
拡散モデルによる自動運転計画に、ハミルトン・ヤコビ到達可能性値関数を「誘導」と「安全シールド」の二重の役割で組み込み、不確実な相互作用下でも安全性と効率を両立させる枠組みを提案。
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著者: Rui Yang, Lei Zheng, Ruoyu Yao, Jun Ma
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
Diffusion models have emerged as a powerful approach for multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent difficulty in enforcing vehicle dynamics and a critical reliance on accurate predictions of other agents, making them prone to safety issues under uncertain interactions. To address these limitations, we introduce DualShield, a planning and control framework that leverages Hamilton-Jacobi (HJ) reachability value functions in a dual capacity. First, the value functions act as proactive guidance, steering the diffusion denoising process towards safe and dynamically feasible regions. Second, they form a reactive safety shield using control barrier-value functions (CBVFs) to modify the executed actions and ensure safety. This dual mechanism preserves the rich exploration capabilities of diffusion models while providing principled safety assurance under uncertain and even adversarial interactions. Simulations in challenging unprotected U-turn scenarios demonstrate that DualShield significantly improves both safety and task efficiency compared to leading methods from different planning paradigms under uncertainty.
