Please use this identifier to cite or link to this item:
Title: Machine Learning Analysis of Pedestrians' Hazard Anticipation from Eye Tracking Data
Authors: Michail, Harris 
Gregoriades, Andreas 
Dimitriou, Loukas 
Pampaka, Maria 
Georgiades, Michael 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Wearable Augmented Reality Displays;Pedestrian safety;Mobile eye tracking;XGBoost classification
Issue Date: 2022
Source: 12th International Workshop on Agents in Traffic and Transportation held in conjunction with IJCAI-ECAI, 2022
Conference: 12th International Workshop on Agents in Traffic and Transportation held in conjunction with IJCAI-ECAI 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.
Rights: ©️ Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Type: Conference Papers
Affiliation : Cyprus University of Technology 
University of Cyprus 
University of Manchester 
Neapolis University Pafos 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

Files in This Item:
File SizeFormat
10.pdf653.14 kBAdobe PDFView/Open
CORE Recommender
Show full item record

Page view(s) 20

Last Week
Last month
checked on Dec 10, 2023

Download(s) 50

checked on Dec 10, 2023

Google ScholarTM


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.