mlsanityData to 14 Oct 2026

hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways

arXiv 2606.27577 ↗First posted 25 Jun 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 85 references that are in the map. First posted on arXiv 25 Jun 2026.

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References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
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  5. 2019Graph Neural Networks for Modelling Traffic Participant Interactioncited by 42
  6. 2021Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Predictioncited by 29
  7. 2021Heterogeneous Edge-Enhanced Graph Attention Network For Multi-Agent Trajectory Predictioncited by 17
  8. 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Reviewcited by 13
  9. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
  10. 2021Towards Traffic Scene Description: The Semantic Scene Graphcited by 12

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