Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories
Esteban Moreno, Patrick Denny, Enda Ward, Jonathan Horgan, Ciarán Eising, Edward Jones, Martin Glavin, Ashkan Parsi, Darragh Mullins, Brian Deegan
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.