3 papers
cs.CV2026
MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Prediction
Md Mahfuzur Rahman, Pengzhan Zhou, A. F. M. Abdun Noor +3
Accurately predicting pedestrian intentions is crucial for ensuring safe and proactive interaction between autonomous vehicles and pedestrians. However, existing approaches often d…
cs.CV2026
Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction
Md Mahfuzur Rahman, Pengzhan Zhou, A F M Abdun Noor +5
Predicting pedestrian crossing intention is a safety-critical task for autonomous driving, yet existing approaches often rely on single-modal inputs or dense multimodal fusion stra…
cs.CV2026
TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction
Md Mustafizur Rahman, Guangchao Yang, A F M Abdun Noor +3
Accurate pedestrian trajectory prediction in crowded environments remains challenging due to the multimodal uncertainty of human motion and the variable complexity of motion dynami…