@article{ref1,
title="Pedestrian crossing intention forecasting at unsignalized intersections using naturalistic trajectories",
journal="Sensors (Basel)",
year="2023",
author="Moreno, Esteban and Denny, Patrick and Ward, Enda and Horgan, Jonathan and Eising, Ciaran and Jones, Edward and Glavin, Martin and Parsi, Ashkan and Mullins, Darragh and Deegan, Brian",
volume="23",
number="5",
pages="e2773-e2773",
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. <br><br>RESULTS show that the model is able to predict crossing intention within a 3-s time window.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23052773",
url="http://dx.doi.org/10.3390/s23052773"
}