Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories

TL;DR

This paper introduces quantitative measures to classify environments as structured or unstructured based on pedestrians' and vehicles' motion trajectories, aiding trajectory prediction in autonomous vehicles.

Contribution

It proposes a data-driven approach using clustering and statistical models to distinguish environment types based on trajectory features, addressing a gap in existing datasets.

Findings

Trajectory variability differs between environment types

Pedestrian stop fraction varies with environment structure

Dataset classification improves trajectory prediction models

Abstract

Trajectory behaviours of pedestrians and vehicles operating close to each other can be different in unstructured compared to structured environments. These differences in the motion behaviour are valuable to be considered in the trajectory prediction algorithm of an autonomous vehicle. However, the available datasets on pedestrians' and vehicles' trajectories that are commonly used as benchmarks for trajectory prediction have not been classified based on the nature of their environment. On the other hand, the definitions provided for unstructured and structured environments are rather qualitative and hard to be used for justifying the type of a given environment. In this paper, we have compared different existing datasets based on a couple of extracted trajectory features, such as mean speed and trajectory variability. Through K-means clustering and generalized linear models, we propose more quantitative measures for distinguishing the two different types of environments. Our results show that features such as trajectory variability, stop fraction and density of pedestrians are different among the two environmental types and can be used to classify the existing datasets.