让自动驾驶更懂危险：生成带风险提示的交通场景图
Hazard-Aware Traffic Scene Graph Generation
- 结合事故数据与深度信息增强视觉特征，识别关键危险源
- 输出彩色标注的场景图，明确标出危险严重性及对自身车辆的影响方式
- 专为驾驶场景设计，适合自动驾驶安全决策系统使用
复杂驾驶场景中的态势感知极具挑战，需持续关注众多场景实体并理解显著危险对自身车辆的影响。现有方法虽能识别特定语义类别和视觉显著区域，却难以评估安全性。传统场景图仅针对前景物体或所有实体建模空间关系，不适用于驾驶场景。为此，我们提出新任务——交通场景图生成，旨在捕捉突出危险与自身车辆之间的交通特异性关系。我们构建了融合交通事故数据与深度线索的框架，补充视觉特征与语义信息以支持推理。输出的交通场景图通过颜色编码突出显示危险严重性，并标注其影响机制及相对于自身车辆的位置。我们在Cityscapes数据集上创建了关系标注，从五个角度在10项任务上评估模型。对比实验与消融研究结果表明，该模型具备出色的以自身为中心的风险感知推理能力。
原文摘要 · Abstract (English)
Maintaining situational awareness in complex driving scenarios is challenging. It requires continuously prioritizing attention among extensive scene entities and understanding how prominent hazards might affect the ego vehicle. While existing studies excel at detecting specific semantic categories and visually salient regions, they lack the ability to assess safety-relevance. Meanwhile, the generic spatial predicates either for foreground objects only or for all scene entities modeled by existing scene graphs are inadequate for driving scenarios. To bridge this gap, we introduce a novel task, Traffic Scene Graph Generation, which captures traffic-specific relations between prominent hazards and the ego vehicle. We propose a novel framework that explicitly uses traffic accident data and depth cues to supplement visual features and semantic information for reasoning. The output traffic scene graphs provide intuitive guidelines that stress prominent hazards by color-coding their severity and notating their effect mechanism and relative location to the ego vehicle. We create relational annotations on Cityscapes dataset and evaluate our model on 10 tasks from 5 perspectives. The results in comparative experiments and ablation studies demonstrate our capacity in ego-centric reasoning for hazard-aware traffic scene understanding.
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