Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks

TL;DR

This paper introduces a transformer-based framework for pedestrian trajectory prediction in urban traffic, integrating positional, interaction, and scene semantics data, outperforming RNN-based methods on real datasets.

Contribution

The work presents a novel transformer-based approach that effectively fuses multiple data types for improved pedestrian trajectory prediction.

Findings

Outperforms baseline methods in short-term predictions

Achieves better long-term trajectory accuracy

Validated on real urban traffic datasets

Abstract

Forecasting the trajectory of pedestrians in shared urban traffic environments is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians' motion trajectories, they were argued to be challenged when dealing with longer sequential data. Thus, in this work, we are introducing a framework based on the transformer networks that were shown recently to be more efficient and outperformed RNNs in many sequential-based tasks. We relied on a fusion of the past positional information, agent interactions information and scene physical semantics information as an input to our framework in order to provide a robust trajectory prediction of pedestrians. We have evaluated our framework on two real-life datasets of pedestrians in shared urban traffic environments and it has outperformed the compared baseline approaches in both short-term and long-term prediction horizons.