Trajectory Unified Transformer for Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction is an essential link to understanding human behavior. Recent work achieves state-of-the-art performance gained from hand-designed post-processing, e.g., clustering. However, this post-processing suffers from expensive inference time and neglects the probability that the predicted trajectory disturbs downstream safety decisions. In this paper, we present Trajectory Unified TRansformer, called TUTR, which unifies the trajectory prediction components, social interaction, and multimodal trajectory prediction, into a transformer encoder-decoder architecture to effectively remove the need for post-processing. Specifically, TUTR parses the relationships across various motion modes using an explicit global prediction and an implicit mode-level transformer encoder. Then, TUTR attends to the social interactions with neighbors by a social-level transformer decoder. Finally, a dual prediction forecasts diverse trajectories and corresponding probabilities in parallel without post-processing. TUTR achieves state-of-the-art accuracy performance and improvements in inference speed of about 10× - 40× compared to previous well-tuned state-of-the-art methods using post-processing.