A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss

Bianca Biebl, Severin Kacianka, Anirudh Unni, Alexander Trende, Jochem W. Rieger, Andreas Lüdtke, Alexander Pretschner, Klaus Bengler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Causal models allow us to reconstruct traffic accidents, predict the likelihood of future accidents and implement counter measures to prevent them. For drivers with impairments like visual field loss, naturalistic data on crash causes is however scarce due to their current prohibition to drive. This paper presents an approach to deriving a causal model for the prediction of crash risks for current non-drivers. The applied use case focuses on a collision with an overlooked crossing vehicle in an intersection. Based on the combination of crash analyses for normal sighted drivers and models of information processing and human errors, a general structural causal model for crash risks in this use case was developed. The application of this model to drivers with visual field loss on the side of the approaching vehicle revealed four causal factors with an increased risk of occurring: faulty anticipation of location and timing of hazards; inadequate guidance of gaze movements; adverse scanning patterns; and cognitive overload. These elevated crash risks can guide the development of assistive technologies for drivers with visual impairments in the future.

Original languageEnglish
Title of host publicationHCI International 2021 - Late Breaking Papers
Subtitle of host publicationHCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings
EditorsConstantine Stephanidis, Vincent G. Duffy, Heidi Krömker, Fiona Fui-Hoon Nah, Keng Siau, Gavriel Salvendy, June Wei
PublisherSpringer Science and Business Media Deutschland GmbH
Pages219-234
Number of pages16
ISBN (Print)9783030909659
DOIs
StatePublished - 2021
Event23rd International Conference on Human-Computer Interaction , HCII 2021 - Virtual, online
Duration: 24 Jul 202129 Jul 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13097 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Human-Computer Interaction , HCII 2021
CityVirtual, online
Period24/07/2129/07/21

Keywords

  • Causal model
  • Crash prediction
  • Interface for disabled and senior people
  • Mobile HCI and automobiles
  • Visual impairments

Fingerprint

Dive into the research topics of 'A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss'. Together they form a unique fingerprint.

Cite this