Loading...
Pedestrian crossing intention forecasting at unsignalized intersections using naturalistic trajectories
Date
2023
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.
Supervisor
Description
Publisher
MDPI
Citation
Sensors 2023, 23, 2273
Collections
Files
Loading...
Denny_2023_Pedestrian.pdf
Adobe PDF, 3.06 MB
Funding code
Funding Information
Sustainable Development Goals
External Link
Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/4.0/