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.

翻译：扩散模型已成为自动驾驶多模态运动规划的一种强大方法。然而，其实际部署通常受到两个固有难题的阻碍：车辆动力学约束难以强制执行，以及对其他智能体预测准确性的严重依赖，这使得模型在不确定交互下容易产生安全问题。为应对这些局限性，我们提出了DualShield——一种利用Hamilton-Jacobi（HJ）可达性值函数双重能力的规划与控制框架。首先，该值函数作为主动引导，将扩散去噪过程导向安全且动力学可行的区域。其次，它们通过控制障碍值函数（CBVF）构成反应式安全屏障，以修正执行动作并确保安全性。这种双重机制在保留扩散模型丰富探索能力的同时，为不确定甚至对抗性交互场景提供了理论化的安全保障。在具有挑战性的无保护U形转弯场景中的仿真实验表明，与不确定性条件下不同规划范式的领先方法相比，DualShield在安全性和任务效率方面均有显著提升。