Citation

Moreno E, Denny P, Ward E, Horgan J, Eising C, Jones E, Glavin M, Parsi A, Mullins D, Deegan B. Sensors (Basel) 2023; 23(5): e2773.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s23052773

PMID

36904976

PMCID

PMC10006956

Abstract

Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian's crossing intention ahead of time will result in safer roads and smoother vehicle maneuvers. The problem of crossing intent forecasting at intersections is formulated in this paper as a classification task. A model that predicts pedestrian crossing behaviour at different locations around an urban intersection is proposed. The model not only provides a classification label (e.g., crossing, not-crossing), but a quantitative confidence level (i.e., probability). The training and evaluation are carried out using naturalistic trajectories provided by a publicly available dataset recorded from a drone.

RESULTS show that the model is able to predict crossing intention within a 3-s time window.

Language: en

Keywords

pedestrian; behaviour; crossing; forecasting; infrastructure; intention estimation