LA-GAT encodes vehicle interactions in dynamic graphs with lane-aware attention bias, pre-trains on NGSIM data then fine-tunes on Chinese UAV merge trajectories, yielding ADE 0.865 m at 1 s and 2.518 m at 3 s on held-out data while tracking TTC and DRAC safety violations.
Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques
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Lane-Aware Graph Attention Network for Multi-Vehicle Trajectory Prediction in Expressway Merge Zones
LA-GAT encodes vehicle interactions in dynamic graphs with lane-aware attention bias, pre-trains on NGSIM data then fine-tunes on Chinese UAV merge trajectories, yielding ADE 0.865 m at 1 s and 2.518 m at 3 s on held-out data while tracking TTC and DRAC safety violations.