用图神经网络预测高速路帧级碰撞风险，效果优于传统方法。
hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways
- 构建包含纵向和横向交互的异构图，融合物理机制特征。
- 在两条高速数据集上平均AUC达0.835（I-80）和0.867（US-101）。
- 可解释每辆车的主要冲突类型，适合实时交通安全管理。
本文将高速路帧级风险评估建模为多智能体场景图上的二分类问题：若任意TTC或PET冲突超过设定阈值，则该帧标记为高风险。每帧构建关系感知图，以车辆为节点，纵向（同车道）和横向（邻车道）交互为两类边，并加入符合追尾与变道冲突机制的物理信息特征。基于非图模型与图基基线的结构化基准，提出HIA-GAT——一种双流异构图注意力网络，通过专用注意力路径处理纵向与横向交互，再通过冲突类型感知门控机制融合，门控信号由SSM冲突归因提供事件级监督。在NGSIM I-80与US-101数据集上，九种TTC/PET阈值配置下，HIA-GAT在平均风险排序性能上表现最佳（I-80 AUC 0.835，US-101 AUC 0.867），尤其在纯PET（变道）设置中提升最显著，凸显关系结构关键作用。除准确率外，学习到的门控机制可提供可解释的每车冲突类型归因，支持可操作的实时高速公路安全监控。结果表明，图结构对横向冲突建模至关重要，而纵向风险常可通过非关系聚合捕捉。
原文摘要 · Abstract (English)
This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajectory frame is labeled risky if any TTC- or PET-based conflict violates a specified severity threshold. We construct a relation-aware graph per frame with vehicles as nodes and two interaction types as edges: same-lane (longitudinal) and adjacent-lane (lateral), augmented with physics-informed edge features aligned to rear-end and lane-change conflict mechanisms. Building on a structured benchmarking suite of non-graph models and graph baselines, we propose HIA-GAT, a dual-stream heterogeneous graph attention network that processes longitudinal and lateral interactions through dedicated attention pathways and fuses them via a conflict-type-aware gating mechanism with event-level gate supervision derived from SSM conflict attribution. Experiments on the NGSIM I-80 and US-101 freeway datasets across nine TTC and PET threshold configurations show that HIA-GAT achieves the best average risk-ranking performance (AUC 0.835 on I-80 and 0.867 on US-101), with the largest gains on PET-only (lane-change) settings where relational structure is essential. Beyond accuracy, the learned gate provides interpretable per-vehicle attribution of dominant conflict type, supporting actionable, real-time freeway safety monitoring. We show that graph structure is critical for modeling lateral conflict risk, while longitudinal risk can often be captured by non-relational aggregation.
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