Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving

2026-06-02

AI Summary

Autonomous driving in complex, dynamic traffic environments often suffers from safety compromises due to the disconnect between prediction uncertainty and motion planning. This work proposes a sample-conditioned differentiable planning framework that, for the first time, directly integrates multimodal trajectories generated by a conditional diffusion model into the planning optimization loop. The approach explicitly controls tail risk through an empirical Conditional Value-at-Risk (CVaR) constraint and incorporates directed graphs for structured scene modeling. Evaluated on the Waymo Open Motion and Argoverse 2 datasets, the method significantly outperforms existing approaches, achieving state-of-the-art performance across key metrics including safety, efficiency, and passenger comfort.

Abstract

Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving, primarily due to the pervasive uncertainty of surrounding traffic. A fundamental bottleneck in current systems is the disconnect between highly expressive uncertainty modeling and interpretable, safe motion planning. In this paper, we propose a novel sample-conditioned differentiable planning framework that bridges this gap by explicitly incorporating diffusion-generated future trajectories into the optimization process. Rather than compressing predictions into a single deterministic future or relying on black-box end-to-end architectures, our approach leverages a conditional diffusion model to generate a diverse set of plausible future scenarios. Crucially, these samples are directly fed into a differentiable planner, which explicitly mitigates predictive uncertainty via an empirical Conditional Value-at-Risk (CVaR) tail-risk constraint. This allows the planner to optimize a physically interpretable trajectory that is robust to rare yet safety-critical interactions. Furthermore, we introduce a directed graph representation for scene context that yields substantial improvements in both predictive effectiveness and computational efficiency. Validated through extensive open-loop and closed-loop evaluations on the Waymo Open Motion and Argoverse 2 datasets, our framework significantly outperforms state-of-the-art baselines in safety, efficiency, and ride comfort.

Problem

• predictive uncertainty
• autonomous driving
• motion planning
• safety
• trajectory prediction

Innovation

• sample-conditioned planning
• differentiable planning
• diffusion model
• Conditional Value-at-Risk (CVaR)
• autonomous driving