Machine Learning Analysis of Pedestrians' Hazard Anticipation from Eye Tracking Data
Date Issued
2022
Abstract
Pedestrian tourists are considered the most vulnerable road users of urban mobility
environments. Tourists are a special category of pedestrians, exhibiting different visual
behaviour to residents due to their enthusiasm and unfamiliarity with the environment. These
characteristics of pedestrian touristsinfluence their hazard perception. Eye tracking technology
became popular in investigating pedestrian safety problems after findings that eye-gaze
behaviour is linked with human attention and hazard anticipation. The majority of eye-tracking
studies to date use stationary technology that may miss out important properties relating to
environmental dynamics that cannot be accurately simulated. This study employs a novel
method utilising mobile eye-tracking technology in naturalistic settings to investigate the
application of machine learning in identifying differences between tourist and resident
pedestrians’ visual behaviour. Eye tracking metrics are used to train an Extreme Gradient Boost
(XGBoost) model to examine whether tourists have less hazard perception than residents when
visiting destinations with opposite driving conventions to their own. Preliminary results with
a small group of tourist and resident pedestrians demonstrate how such machine learning
models could be used in real-time by agent-based systems that utilise wearable augmented
reality displays to support hazard perception of tourist pedestrians.
environments. Tourists are a special category of pedestrians, exhibiting different visual
behaviour to residents due to their enthusiasm and unfamiliarity with the environment. These
characteristics of pedestrian touristsinfluence their hazard perception. Eye tracking technology
became popular in investigating pedestrian safety problems after findings that eye-gaze
behaviour is linked with human attention and hazard anticipation. The majority of eye-tracking
studies to date use stationary technology that may miss out important properties relating to
environmental dynamics that cannot be accurately simulated. This study employs a novel
method utilising mobile eye-tracking technology in naturalistic settings to investigate the
application of machine learning in identifying differences between tourist and resident
pedestrians’ visual behaviour. Eye tracking metrics are used to train an Extreme Gradient Boost
(XGBoost) model to examine whether tourists have less hazard perception than residents when
visiting destinations with opposite driving conventions to their own. Preliminary results with
a small group of tourist and resident pedestrians demonstrate how such machine learning
models could be used in real-time by agent-based systems that utilise wearable augmented
reality displays to support hazard perception of tourist pedestrians.
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