When Do They Cross? Temporal Dynamics of Pedestrian Intention Prediction and Crossing Actions

Abstract

Pedestrian intention prediction is critical for safe and socially intelligent autonomous vehicle (AV) operation in urban environments. While many existing models assume that pedestrian actions directly reflect intentions, the temporal dynamics between inferred intentions and observable actions remain underexplored. This study investigates the temporal relationship between inferred intention and actions of pedestrians. We used the Pedestrian Situated Intention (PSI) dataset, which includes frame-wise annotations of pedestrian behavior and aggregated human inferences of crossing intention from 24 raters. We analyzed time lag and the influence of preparatory actions like looking across five behavioral categories, such as pedestrians crossing from standing or not crossing by slowing down. Findings suggest that intention prediction, timing and looking behavior varies based on the specific scenario and that critical actions such as greeting can help predict intentions. The goal is to develop better models that allow autonomous vehicles more time to react to pedestrian movements.

Keywords

pedestrian intention prediction human behavior modeling event segmentation

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