2 papers
cs.LG2026
Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction
Jiazhao Shi, Qiyang Xie, Ziyu Wang +7
Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depends on evolving traffic risk, sur…
cs.CV2025
GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction
Chen Xie, Ciyun Lin, Xiaoyu Zheng +2
Understanding and predicting pedestrian crossing behavioral intention is crucial for the driving safety of autonomous vehicles. Nonetheless, challenges emerge when using promising…