Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction

no code implementations • 26 Nov 2021 • Jie Wang, Caili Guo, Minan Guo, Jiujiu Chen

JAL-MTP use a Social to Lane (S2L) module to jointly represent the static lane and the dynamic motion of the neighboring agents as instance-level lane, a Recurrent Lane Attention (RLA) mechanism for utilizing the instance-level lanes to predict the map-adaptive future trajectories and two selectors to identify the typical and reasonable trajectories.

Autonomous Vehicles Trajectory Prediction