Predicting Human Trajectories by Learning and Matching Patterns

TL;DR

This paper introduces Social-PEC, a CNN-based method for learning and matching human movement patterns to predict trajectories, improving interpretability and performance in human-robot interaction scenarios.

Contribution

The paper presents a novel CNN approach that not only predicts human trajectories but also enhances interpretability by revealing pattern detection mechanisms.

Findings

Performs comparably to state-of-the-art methods

Outperforms in certain trajectory prediction cases

Provides a more interpretable model of decision-making

Abstract

As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others' trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based approach to learn, detect, and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly, the proposed approach unveils the obscurity in the previous use of a pooling layer, presenting a way to intuitively explain the decision-making process.