MapFlow: Multi-Agent Pedestrian Trajectory Prediction Using Normalizing Flow

Abstract

In the task of pedestrian trajectory prediction, multi-modal prediction has recently emerged, demonstrating how a good model should predict multiple socially acceptable futures. With this respect, Normalizing Flows (NFs) have shown remarkable generative capabilities that make them particularly suitable for multi-modal trajectory prediction. By sampling from the learned distribution, NFs can produce multiple socially acceptable trajectories, each one paired with its corresponding likelihood score. Taking advantage of the multi-modal prediction coupled with the likelihood score, with MapFlow we introduce a solution based on NFs that improves the accuracy in prediction by incorporating in the model the social influence of neighboring pedestrians. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>