arXiv 2606.032962026-06-02cs.RO

桥接预测不确定性与安全行动：面向自动驾驶的样本条件可微分规划

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

Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology（香港科学与技术大学机器人与自主系统方向）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学电子与计算机工程系）；Department of Civil and Environmental Engineering, University of California, Los Angeles（加州大学洛杉矶分校土木与环境工程系）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）

Chengzhen Meng, Pei Liu, Zhiyu Huang, Chen Lv, Jun Ma

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AI总结：

提出一种样本条件可微分规划框架，通过扩散模型生成多样未来场景并直接输入可微分规划器，利用条件风险价值约束缓解预测不确定性，实现安全、高效、舒适的自动驾驶运动规划。

摘要：

复杂、动态且交互的驾驶环境给自动驾驶带来了重大挑战，主要源于周围交通的普遍不确定性。当前系统的一个基本瓶颈是高度表达性的不确定性建模与可解释、安全的运动规划之间的脱节。在本文中，我们提出了一种新颖的样本条件可微分规划框架，通过将扩散生成的未来轨迹显式纳入优化过程来弥合这一差距。我们的方法不是将预测压缩为单一的确定性未来或依赖黑盒端到端架构，而是利用条件扩散模型生成一组多样化的合理未来场景。关键的是，这些样本直接输入可微分规划器，该规划器通过经验条件风险价值尾部风险约束显式缓解预测不确定性。这使得规划器能够优化一条物理可解释的轨迹，该轨迹对罕见但安全关键的交互具有鲁棒性。此外，我们引入了一种场景上下文的有向图表示，在预测有效性和计算效率方面均带来了显著提升。通过在Waymo Open Motion和Argoverse 2数据集上进行的大量开环和闭环评估，我们的框架在安全性、效率和乘坐舒适性方面显著优于最先进的基线方法。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。