Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories

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.

Keywords: behaviour; crossing; forecasting; infrastructure; intention estimation; pedestrian.

Figure 1

Segmented intersection. Blue represents the sidewalk and red the road.

Figure 2

Distance to the road from current pedestrian position. The pedestrian current position is represented by the point shown in green and the heading direction by the white line.

Figure 3

Dataset containing the derived categorical feature crossing for recording #18. (a) Imbalance among classes; (b) color-coded trajectories of C and NC trajectory samples.

Figure 4

Confusion matrix for RF classification for test set (recordings #19 to #29).

Figure 5

Classification results for 10 crossing trajectories. Pedestrian trajectories are shown in magenta. The trajectory point shown in yellow indicates the last trajectory sample in the sequence.

Figure 6

Classification results for 10 not crossing trajectories. Pedestrian trajectories are shown in magenta. The trajectory point shown in yellow indicates the last trajectory sample in the sequence.

Figure 7

Confusion matrix for RF classification for all samples at recording #18.

Figure 8

Intent prediction and velocity (plus approaching vehicle velocity) for pedestrians 53 (left) and 298 (right) from recording #18.