发表机构
University of Tübingen（图宾根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出面向弱势道路使用者的关键性度量及场景无关预测框架，在DeepAccident数据集上分别提升分类性能50%和超越现有方法275%，实现F1分数0.96。
AI 中文摘要
提高安全性是自动驾驶车辆的首要目标。实现这一目标需要可靠的安全度量，这些度量应包含与安全相关的因素，如物体类型、速度和关键性。此类度量的一个关键能力是区分关键物体和非关键物体，这通过关键性或相关性估计来解决。现有的关键性度量通常针对特定场景设计，并主要关注车辆与车辆之间的交互。因此，在本文中，我们提出了一种针对弱势道路使用者（VRU）的新型关键性度量，由于其运动行为较难预测，需要特别考虑。此外，为避免场景特定度量带来的复杂性，我们引入了一个适用于所有交通参与者类别的场景无关关键性预测框架。所提出的以VRU为中心的关键性度量和关键性预测框架的有效性使用DeepAccident数据集进行评估，该数据集包含多种安全关键的交通场景。所提出的以VRU为中心的关键性度量将行人关键性分类性能提高了高达50%。此外，所提出的关键性预测框架在性能上优于最先进的度量方法达275%，实现了0.96的F1分数，并能够对所有物体类别进行场景无关的关键性评估。这些结果表明，所提出的方法在增强自动驾驶系统安全评估的关键性评估方面具有巨大潜力。
英文摘要
Increasing safety is the primary objective of automated vehicles. Achieving this goal requires reliable safety metrics that incorporate safety-relevant factors such as object type, velocity, and criticality. A key capability of such metrics is the distinction between critical and non-critical objects, which is addressed through criticality or relevance estimation. Existing criticality metrics are typically designed for specific scenarios and primarily focus on vehicle-to-vehicle interactions. In this paper, we therefore propose a novel criticality metric tailored to vulnerable road users (VRUs), which require special consideration due to their less predictable motion behavior. Furthermore, to avoid the complexity introduced by scenario-specific metrics, we introduce a scenario-independent criticality prediction framework applicable to all traffic participant classes. The effectiveness of both the proposed VRU-centric criticality metric and the criticality prediction framework is evaluated using the DeepAccident dataset, which contains a diverse set of safety-critical traffic scenarios. The proposed VRU-centric criticality metric improves pedestrian criticality classification performance by up to 50 %. In addition, the proposed criticality prediction framework outperforms state-of-the-art metrics by 275 %, achieving an F1-score of 0.96 and enabling scenario-independent criticality assessment across all object classes. These results demonstrate the strong potential of the proposed approaches to enhance criticality assessment for safety evaluation in automated driving systems.
CommentsAccepted at IEEE VTC Fall 2026
