# DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving arXiv: https://arxiv.org/abs/2601.15729 ยท HTML: https://mlsanity.com/paper/2601.15729/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. - Builds on 33 of its 42 references that are in the map. - First posted on arXiv 22 Jan 2026. ## Placed in - Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/) ## Builds on (most cited first, 30 of 33) - [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022) - [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019) - [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023) - [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022) - [Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models](https://mlsanity.com/paper/2401.03360/) (2023) - [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017) - [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023) - [Robust Control Barrier-Value Functions for Safety-Critical Control](https://mlsanity.com/paper/2104.02808/) (2021) - [CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions](https://mlsanity.com/paper/2406.05309/) (2024) - [Model-Based Diffusion for Trajectory Optimization](https://mlsanity.com/paper/2407.01573/) (2024) - [On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions](https://mlsanity.com/paper/2012.03390/) (2020) - [CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning](https://mlsanity.com/paper/2405.01758/) (2024) - [Refining Control Barrier Functions through Hamilton-Jacobi Reachability](https://mlsanity.com/paper/2204.12507/) (2022) - [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023) - [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021) - [CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model](https://mlsanity.com/paper/2412.04209/) (2024) - [Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing](https://mlsanity.com/paper/2505.13131/) (2025) - [Joint Model-based Model-free Diffusion for Planning with Constraints](https://mlsanity.com/paper/2509.08775/) (2025) - [Trajectory Distribution Control for Model Predictive Path Integral Control using Covariance Steering](https://mlsanity.com/paper/2109.12147/) (2021) - [Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Approach Using Control Barrier Functions](https://mlsanity.com/paper/2302.11719/) (2023) - [Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers](https://mlsanity.com/paper/2401.09241/) (2024) - [Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy](https://mlsanity.com/paper/2306.12369/) (2023) - [Spatiotemporal Receding Horizon Control with Proactive Interaction Towards Autonomous Driving in Dense Traffic](https://mlsanity.com/paper/2308.05929/) (2023) - [Equality Constrained Diffusion for Direct Trajectory Optimization](https://mlsanity.com/paper/2410.01939/) (2024) - [Automated Lane Merging via Game Theory and Branch Model Predictive Control](https://mlsanity.com/paper/2311.14916/) (2023) - [Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving](https://mlsanity.com/paper/2402.10441/) (2024) - [Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections](https://mlsanity.com/paper/2412.00162/) (2024) - [DualGuard MPPI: Safe and Performant Optimal Control by Combining Sampling-Based MPC and Hamilton-Jacobi Reachability](https://mlsanity.com/paper/2502.01924/) (2025) - [Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways](https://mlsanity.com/paper/2310.07526/) (2023) - [Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions](https://mlsanity.com/paper/2008.00067/) (2020) ## Built on by (most cited first, 1 of 1) - [Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models](https://mlsanity.com/paper/2605.24924/) (2026) ## Abstract (arXiv) 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.