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.AI2024
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
Yuepeng He, Pengzhan Zhou, Yijun Zhai +4
Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to va…
cs.DC2024
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
Yijun Zhai, Pengzhan Zhou, Yuepeng He +5
The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great pote…