Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance.


Journal

Accident; analysis and prevention
ISSN: 1879-2057
Titre abrégé: Accid Anal Prev
Pays: England
ID NLM: 1254476

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 29 06 2022
revised: 03 06 2023
accepted: 27 06 2023
medline: 7 8 2023
pubmed: 14 7 2023
entrez: 13 7 2023
Statut: ppublish

Résumé

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

Identifiants

pubmed: 37441985
pii: S0001-4575(23)00242-7
doi: 10.1016/j.aap.2023.107195
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

107195

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Roja Ezzati Amini (R)

Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, 85748, Germany. Electronic address: roja.ezzati@tum.de.

Christelle Al Haddad (C)

Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, 85748, Germany.

Debapreet Batabyal (D)

Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, 85748, Germany.

Isidora Gkena (I)

Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, 85748, Germany.

Bart De Vos (B)

DriveSimSolutions, Diepenbeek, Belgium.

Ariane Cuenen (A)

School for Transportation Sciences, Transportation Research Institute, UHasselt, Diepenbeek, Belgium.

Tom Brijs (T)

School for Transportation Sciences, Transportation Research Institute, UHasselt, Diepenbeek, Belgium.

Constantinos Antoniou (C)

Chair of Transportation Systems Engineering, TUM School of Engineering and Design, Technical University of Munich, Munich, 85748, Germany.

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Classifications MeSH