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Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Ghadah Alosaimi, Hanadi Alhamdan, Wenke E +3
Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance systems, yet remains challenging…
BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
Ruochen Li, Stamos Katsigiannis, Tae-Kyun Kim +1
Trajectory prediction allows better decision-making in applications of autonomous vehicles or surveillance by predicting the short-term future movement of traffic agents. It is cla…
Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
Ruochen Li, Tanqiu Qiao, Stamos Katsigiannis +2
Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedest…
Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding
Ruochen Li, Stamos Katsigiannis, Hubert P. H. Shum
Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been…