DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving

TL;DR

DualShield integrates reachability analysis with diffusion models to enhance safety and feasibility in autonomous driving, effectively managing uncertain and adversarial interactions while maintaining exploration capabilities.

Contribution

It introduces a dual safety mechanism using Hamilton-Jacobi reachability and control barrier functions to ensure safety in diffusion-based motion planning.

Findings

Significantly improves safety in challenging scenarios

Enhances task efficiency under uncertainty

Maintains rich exploration capabilities

Abstract

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.