在部分可观测环境下学习自动驾驶的统一风险地图

摘要

遮挡感知预测由于未观测区域固有的不确定性，仍是自动驾驶中的关键挑战。现有方法或基于可达状态高估风险，或在高度遮挡不确定性下难以准确预测轨迹。针对这些局限，我们提出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。为解决遮挡交互场景稀缺的问题，我们引入一种基于扩散的场景生成框架，能够生成真实且具有对抗性的场景。我们将统一风险地图的建模与学习集成到一个框架中，支持部分可观测条件下的风险感知规划。在Waymo开放运动数据集上的实验表明，我们的方法显著优于当前最先进的遮挡感知基线，将最小碰撞时间提升了0.78倍，平均碰撞时间提升了1.67倍。所提出的框架为部分可观测环境下的风险感知规划提供了一种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.