graph-attention-network

This paper proposes HIA-GAT, a dual-stream heterogeneous graph attention network that integrates longitudinal and lateral vehicle interactions with a conflict-type-aware gating mechanism for frame-level traffic conflict risk prediction on freeways. Experiments on NGSIM datasets show improved risk-ranking performance, particularly for lateral conflicts, and provide interpretable per-vehicle conflict attribution.