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.
A perspective from competitive-cooperative driving modes: Identification of vehicle merging behavior models and crash risk factors in merge zone
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Gradient- and perturbation-based XAI methods show substantial agreement on frontal, temporal, and posterior EEG regions for an InceptionTime MDD classifier, while DeepSHAP differs, with overall partial convergence and method-dependent variability.
citing papers explorer
-
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.
-
Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection
Gradient- and perturbation-based XAI methods show substantial agreement on frontal, temporal, and posterior EEG regions for an InceptionTime MDD classifier, while DeepSHAP differs, with overall partial convergence and method-dependent variability.