构建统一风险图谱，提升自动驾驶在遮挡环境下的安全决策能力。
Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
- 融合交通流与碰撞风险的时空建模，精细评估遮挡带来的隐患。
- 生成逼真对抗性场景，解决遮挡交互数据稀缺问题，提升模型鲁棒性。
- 在Waymo数据集上显著改善时间到碰撞指标，适合高阶自动驾驶系统使用。
由于未观测区域固有的不确定性，遮挡感知预测仍是自动驾驶中的关键挑战。现有方法或因可达状态高估风险，或在高遮挡不确定性下难以准确预测轨迹。为此，本文提出一种面向部分可观测环境的统一风险图谱建模与学习框架。通过时空建模融合交通流风险与碰撞风险，实现对遮挡引发危险的细粒度评估。针对遮挡交互场景稀缺问题，引入基于扩散模型的场景生成框架，合成真实且具有挑战性的对抗性场景。将风险图谱建模与学习集成至支持部分可观测环境下风险感知规划的框架中。在Waymo Open Motion Dataset上的实验表明，该方法显著优于当前最优的遮挡感知基线，最小时间到碰撞提升0.78倍，平均时间到碰撞提升1.67倍。所提框架为部分可观测环境下的风险感知规划提供了全面且实用的解决方案。
原文摘要 · Abstract (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.
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