W2W: Language-Model-Based Trajectory Prediction with Reinforcement Learning
This work converts observed trajectories and interaction cues into parsable textual prompts, so that interaction semantics are expressed more explicitly in the model input and remains competitive with recent LM-based prediction methods and strong trajectory prediction base-lines on ADE/FDE.
Zi-Rui Xu, Biao Yang, Rongrong Ni et al.
· 1 citation