在部分可觀測環境中學習自動駕駛的統一風險圖

摘要

遮挡感知预测因未观测区域的固有不确定性，始终是自动驾驶领域的关键挑战。现有方法要么基于可达状态高估风险，要么在高遮挡不确定性下难以准确预测轨迹。为克服这些局限，我们提出了一种面向部分可观测环境的统一风险地图建模与学习框架。该方法通过时空建模整合交通流风险与碰撞风险，实现对遮挡引发风险的细粒度评估。针对遮挡交互场景稀缺的问题，我们引入基于扩散模型的场景生成框架，可生成真实且具对抗性的场景。我们将统一风险地图的建模与学习集成至框架中，支持部分可观测环境下的风险感知规划。在Waymo开放运动数据集上的实验表明，该方法显著优于现有遮挡感知基线模型，将最小碰撞时间提升0.78倍，平均碰撞时间提升1.67倍。所提框架为部分可观测环境中的风险感知规划提供了全面且实用的解决方案。

English

Occlusion-aware prediction remains a critical challenge in autonomous driving due to the inherent uncertainty of unobserved regions. Existing approaches either overestimate risk based on reachable states or struggle to predict accurate trajectories under high occlusion uncertainty. To address these limitations, we propose a unified risk map modeling and learning framework for partially observable environments. Our method integrates traffic flow risk and collision risk through spatiotemporal modeling, enabling fine-grained assessment of occlusion-induced hazards. To address the scarcity of scenarios involving occluded interactions, we introduce a diffusion-based scenario generation framework that produces realistic yet adversarial scenarios. We integrate the modeling and learning of a unified risk map into a framework that supports risk-aware planning under partial observability. Experiments on the Waymo Open Motion Dataset show that our method significantly outperforms the state-of-the-art occlusion-aware baseline, improving minimum time-to-collision by 0.78 times and average time-to-collision by 1.67 times. The proposed framework offers a comprehensive and practical solution for risk-aware planning in partially observable environments.