Modelling Pedestrian Behaviour in Autonomous Vehicle Encounters Using Naturalistic Dataset

TL;DR

This paper analyzes pedestrian behavior in autonomous vehicle encounters using a hybrid modeling approach on a naturalistic dataset, revealing asymmetries in risk perception and movement adjustments.

Contribution

It introduces a novel hybrid discrete choice-machine learning framework to model pedestrian behavior with detailed perceptual and kinematic indicators.

Findings

Strong directional asymmetries in risk perception influence pedestrian movement

Mid-crossing thresholds are identified for remaining distance cues

Relative speed has a lesser impact on pedestrian behavior

Abstract

Understanding how pedestrians adjust their movement when interacting with autonomous vehicles (AVs) is essential for improving safety in mixed traffic. This study examines micro-level pedestrian behaviour during midblock encounters in the NuScenes dataset using a hybrid discrete choice-machine learning framework based on the Residual Logit (ResLogit) model. The model incorporates temporal, spatial, kinematic, and perceptual indicators. These include relative speed, visual looming, remaining distance, and directional collision risk proximity (CRP) measures. Results suggest that some of these variables may meaningfully influence movement adjustments, although predictive performance remains moderate. Marginal effects and elasticities indicate strong directional asymmetries in risk perception, with frontal and rear CRP showing opposite influences. The remaining distance exhibits a possible mid-crossing threshold. Relative speed cues appear to have a comparatively less effect. These patterns may reflect multiple behavioural tendencies driven by both risk perception and movement efficiency.