用虚拟车辆追踪遮挡区，提升自动驾驶城市驾驶安全性
From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving
- 引入序列推理的虚拟车辆模型，持续追踪遮挡区域
- 仿真显示情境感知能力提升，安全与通行效率更好平衡
- 适合关注自动驾驶风险规划的研究者和工程师
自动驾驶车辆在动态城市环境中需应对遮挡与感知局限带来的不确定性。现有研究虽分别发展了遮挡追踪与风险评估方法，但尚未形成系统性整合方案。本文通过增强以虚拟车辆为中心的模型，引入序列推理机制，实现对遮挡区域的持续追踪与潜在危险预测。通过建模具有不同行为特征的多样化虚拟车辆，该方法能够更真实地呈现复杂场景，并支持上下文感知的风险评估。仿真结果表明，所提方法显著提升了情境感知能力，在主动安全与交通流效率间取得更好平衡。尽管如此，仍需在真实场景中验证其可行性与泛化能力。本工作基于成熟方法进行改进，为复杂城市环境下的自动驾驶规划提供了更安全可靠的解决方案。相关代码已开源：https://github.com/TUM-AVS/OcclusionAwareMotionPlanning
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) must navigate dynamic urban environments where occlusions and perception limitations introduce significant uncertainties. This research builds upon and extends existing approaches in risk-aware motion planning and occlusion tracking to address these challenges. While prior studies have developed individual methods for occlusion tracking and risk assessment, a comprehensive method integrating these techniques has not been fully explored. We, therefore, enhance a phantom agent-centric model by incorporating sequential reasoning to track occluded areas and predict potential hazards. Our model enables realistic scenario representation and context-aware risk evaluation by modeling diverse phantom agents, each with distinct behavior profiles. Simulations demonstrate that the proposed approach improves situational awareness and balances proactive safety with efficient traffic flow. While these results underline the potential of our method, validation in real-world scenarios is necessary to confirm its feasibility and generalizability. By utilizing and advancing established methodologies, this work contributes to safer and more reliable AV planning in complex urban environments. To support further research, our method is available as open-source software at: https://github.com/TUM-AVS/OcclusionAwareMotionPlanning
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