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

doi:10.1007/978-3-030-90966-6_16

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

Technical University of Munich
Universität Oldenburg
OFFIS Institute for Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 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 language	English
Title of host publication	HCI International 2021 - Late Breaking Papers
Subtitle of host publication	HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings
Editors	Constantine Stephanidis, Vincent G. Duffy, Heidi Krömker, Fiona Fui-Hoon Nah, Keng Siau, Gavriel Salvendy, June Wei
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	219-234
Number of pages	16
ISBN (Print)	9783030909659
DOIs	https://doi.org/10.1007/978-3-030-90966-6_16
State	Published - 2021
Event	23rd International Conference on Human-Computer Interaction , HCII 2021 - Virtual, online Duration: 24 Jul 2021 → 29 Jul 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13097 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Human-Computer Interaction , HCII 2021
City	Virtual, online
Period	24/07/21 → 29/07/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1007/978-3-030-90966-6_16

Cite this

Biebl, B., Kacianka, S., Unni, A., Trende, A., Rieger, J. W., Lüdtke, A., Pretschner, A., & Bengler, K. (2021). A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss. In C. Stephanidis, V. G. Duffy, H. Krömker, F. Fui-Hoon Nah, K. Siau, G. Salvendy, & J. Wei (Eds.), HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings (pp. 219-234). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13097 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90966-6_16

Biebl, Bianca ; Kacianka, Severin ; Unni, Anirudh et al. / A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss. HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings. editor / Constantine Stephanidis ; Vincent G. Duffy ; Heidi Krömker ; Fiona Fui-Hoon Nah ; Keng Siau ; Gavriel Salvendy ; June Wei. Springer Science and Business Media Deutschland GmbH, 2021. pp. 219-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b35f91c75d264e5b9f6ba0809e5478dd,

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

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.",

keywords = "Causal model, Crash prediction, Interface for disabled and senior people, Mobile HCI and automobiles, Visual impairments",

author = "Bianca Biebl and Severin Kacianka and Anirudh Unni and Alexander Trende and Rieger, \{Jochem W.\} and Andreas L{\"u}dtke and Alexander Pretschner and Klaus Bengler",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Human-Computer Interaction , HCII 2021 ; Conference date: 24-07-2021 Through 29-07-2021",

year = "2021",

doi = "10.1007/978-3-030-90966-6\_16",

language = "English",

isbn = "9783030909659",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "219--234",

editor = "Constantine Stephanidis and Duffy, \{Vincent G.\} and Heidi Kr{\"o}mker and \{Fui-Hoon Nah\}, Fiona and Keng Siau and Gavriel Salvendy and June Wei",

booktitle = "HCI International 2021 - Late Breaking Papers",

}

Biebl, B, Kacianka, S, Unni, A, Trende, A, Rieger, JW, Lüdtke, A, Pretschner, A & Bengler, K 2021, A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss. in C Stephanidis, VG Duffy, H Krömker, F Fui-Hoon Nah, K Siau, G Salvendy & J Wei (eds), HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13097 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 219-234, 23rd International Conference on Human-Computer Interaction , HCII 2021, Virtual, online, 24/07/21. https://doi.org/10.1007/978-3-030-90966-6_16

A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss. / Biebl, Bianca; Kacianka, Severin; Unni, Anirudh et al.
HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings. ed. / Constantine Stephanidis; Vincent G. Duffy; Heidi Krömker; Fiona Fui-Hoon Nah; Keng Siau; Gavriel Salvendy; June Wei. Springer Science and Business Media Deutschland GmbH, 2021. p. 219-234 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13097 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss

AU - Biebl, Bianca

AU - Kacianka, Severin

AU - Unni, Anirudh

AU - Trende, Alexander

AU - Rieger, Jochem W.

AU - Lüdtke, Andreas

AU - Pretschner, Alexander

AU - Bengler, Klaus

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Causal model

KW - Crash prediction

KW - Interface for disabled and senior people

KW - Mobile HCI and automobiles

KW - Visual impairments

UR - https://www.scopus.com/pages/publications/85120626133

U2 - 10.1007/978-3-030-90966-6_16

DO - 10.1007/978-3-030-90966-6_16

M3 - Conference contribution

AN - SCOPUS:85120626133

SN - 9783030909659

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 219

EP - 234

BT - HCI International 2021 - Late Breaking Papers

A2 - Stephanidis, Constantine

A2 - Duffy, Vincent G.

A2 - Krömker, Heidi

A2 - Fui-Hoon Nah, Fiona

A2 - Siau, Keng

A2 - Salvendy, Gavriel

A2 - Wei, June

PB - Springer Science and Business Media Deutschland GmbH

T2 - 23rd International Conference on Human-Computer Interaction , HCII 2021

Y2 - 24 July 2021 through 29 July 2021

ER -

Biebl B, Kacianka S, Unni A, Trende A, Rieger JW, Lüdtke A et al. A Causal Model of Intersection-Related Collisions for Drivers With and Without Visual Field Loss. In Stephanidis C, Duffy VG, Krömker H, Fui-Hoon Nah F, Siau K, Salvendy G, Wei J, editors, HCI International 2021 - Late Breaking Papers: HCI Applications in Health, Transport, and Industry - 23rd HCI International Conference, HCII 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 219-234. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-90966-6_16

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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