Dynamic Scene Graph Framework Improves Multi-Vehicle Lane-Change Prediction

Jul 14, 2026

Researchers propose DSiGAT, a dynamic scene graph attention framework that predicts lane-change intentions and trajectories for all vehicles in a traffic scene simultaneously. The model achieves intention prediction accuracies above 90% on NGSIM datasets and reduces trajectory RMSE by up to 52.94% compared to baselines. The approach uses temporal graph-attention message passing and an intention-guided decoder to produce coherent multi-vehicle future predictions.

Why it matters: This work advances autonomous vehicle safety by enabling more accurate and coherent prediction of multiple vehicles' maneuvers, which is critical for safe motion planning in complex traffic scenes.

Full story at: arXiv AI/ML ↗