Context-Aware Pedestrian Motion Prediction In Urban Intersections

TL;DR

This paper introduces a context-aware pedestrian motion prediction method for urban intersections that incorporates environmental semantic features, leading to improved accuracy and confidence in trajectory predictions.

Contribution

It extends previous Markovian and clustering-based motion primitives by integrating environmental semantics into Gaussian Process models for better prediction accuracy.

Findings

12.5% improvement in prediction accuracy

2.65 times reduction in AUC metric

Effective in real-world urban intersection data

Abstract

This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-based world and subsequently predict pedestrian trajectories by modeling the transition between learned primitives as a Gaussian Process (GP). This work extends that prior approach by incorporating semantic features from the environment (relative distance to curbside and status of pedestrian traffic lights) in the GP formulation for more accurate predictions of pedestrian trajectories over the same timescale. We evaluate the new approach on real-world data collected using one of the vehicles in the MIT Mobility On Demand fleet. The results show 12.5% improvement in prediction accuracy and a 2.65 times reduction in Area Under the Curve (AUC), which is used as a metric to quantify the span of predicted set of trajectories, such that a lower AUC corresponds to a higher level of confidence in the future direction of pedestrian motion.