DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
- URL: http://arxiv.org/abs/2601.15729v1
- Date: Thu, 22 Jan 2026 07:56:36 GMT
- Title: DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
- Authors: Rui Yang, Lei Zheng, Ruoyu Yao, Jun Ma,
- Abstract summary: We introduce DualShield, a planning and control framework that leverages Hamilton-Jacobi (HJ) reachability value functions in a dual capacity.<n>First, the value functions act as proactive guidance, steering the diffusion denoising process towards safe and dynamically feasible regions.<n>Second, they form a reactive safety shield using control barrier-value functions (CBVFs) to modify the executed actions and ensure safety.
- Score: 8.323621563740772
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
arXiv Detail & Related papers (2026-02-23T18:19:46Z) - Self-Guard: Defending Large Reasoning Models via enhanced self-reflection [54.775612141528164]
Self-Guard is a lightweight safety defense framework for Large Reasoning Models.<n>It bridges the awareness-compliance gap, achieving robust safety performance without compromising model utility.<n>Self-Guard exhibits strong generalization across diverse unseen risks and varying model scales.
arXiv Detail & Related papers (2026-01-31T13:06:11Z) - Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving [11.62520853262219]
We introduce criticality-aware robust RL (CARRL) for handling sparse, safety-critical risks in autonomous driving.<n>CARRL consists of two interacting components: a risk exposure adversary (REA) and a risk-targeted robust agent (RTRA)<n>We show that our approach reduces the collision rate by at least 22.66% across all cases compared to state-of-the-art baseline methods.
arXiv Detail & Related papers (2026-01-05T05:20:16Z) - Automating Steering for Safe Multimodal Large Language Models [58.36932318051907]
We introduce a modular and adaptive inference-time intervention technology, AutoSteer, without requiring any fine-tuning of the underlying model.<n>AutoSteer incorporates three core components: (1) a novel Safety Awareness Score (SAS) that automatically identifies the most safety-relevant distinctions among the model's internal layers; (2) an adaptive safety prober trained to estimate the likelihood of toxic outputs from intermediate representations; and (3) a lightweight Refusal Head that selectively intervenes to modulate generation when safety risks are detected.
arXiv Detail & Related papers (2025-07-17T16:04:55Z) - Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling [74.41886258801209]
We propose a two-stage trajectory planning framework that decouples principle alignment from behavior learning.<n>Plan-R1 significantly improves planning safety and feasibility, achieving state-of-the-art performance.
arXiv Detail & Related papers (2025-05-23T09:22:19Z) - Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections [9.041849642602626]
Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles.<n>This study proposes a safety-critical planning method that integrates Dynamic High-Order Control Barrier Functions (DHOCBF) with a diffusion-based model, called DSC-Diffuser.<n>To further ensure driving safety in dynamic environments, the proposed DHOCBF framework dynamically adjusts to account for the movements of surrounding vehicles.
arXiv Detail & Related papers (2024-11-29T11:57:00Z) - Uniformly Safe RL with Objective Suppression for Multi-Constraint Safety-Critical Applications [73.58451824894568]
The widely adopted CMDP model constrains the risks in expectation, which makes room for dangerous behaviors in long-tail states.
In safety-critical domains, such behaviors could lead to disastrous outcomes.
We propose Objective Suppression, a novel method that adaptively suppresses the task reward maximizing objectives according to a safety critic.
arXiv Detail & Related papers (2024-02-23T23:22:06Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework.
Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations.
We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - Active Uncertainty Reduction for Safe and Efficient Interaction
Planning: A Shielding-Aware Dual Control Approach [9.07774184840379]
We present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm.
Our approach relies on sampling-based approximation of dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods.
arXiv Detail & Related papers (2023-02-01T01:34:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.