An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis.
autonomous vehicles
behavioral modeling
brain functional network
graphic theoretical analysis
trust in automation
Journal
Frontiers in neuroinformatics
ISSN: 1662-5196
Titre abrégé: Front Neuroinform
Pays: Switzerland
ID NLM: 101477957
Informations de publication
Date de publication:
2022
2022
Historique:
received:
05
04
2022
accepted:
16
06
2022
entrez:
2
9
2022
pubmed:
3
9
2022
medline:
3
9
2022
Statut:
epublish
Résumé
With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.
Identifiants
pubmed: 36051853
doi: 10.3389/fninf.2022.907942
pmc: PMC9426721
doi:
Types de publication
Journal Article
Langues
eng
Pagination
907942Informations de copyright
Copyright © 2022 Xu, Dragomir, Liu, Yin, Wan, Bezerianos and Wang.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer JZ declared a shared affiliation with the authors XL and FW to the handling editor at the time of review.
Références
Hum Factors. 2004 Spring;46(1):50-80
pubmed: 15151155
Front Hum Neurosci. 2017 May 12;11:237
pubmed: 28553215
PLoS One. 2014 May 07;9(5):e96505
pubmed: 24806295
Nonlinear Biomed Phys. 2007 Jul 05;1(1):3
pubmed: 17908336
Crit Care. 2016 Apr 20;20(Suppl 2):94
pubmed: 27885969
Hum Factors. 2020 Aug;62(5):718-736
pubmed: 31233695
Cortex. 2013 Oct;49(9):2424-38
pubmed: 23489778
Hum Factors. 2016 May;58(3):509-19
pubmed: 26843570
J Air Waste Manag Assoc. 2020 Jul;70(7):670-686
pubmed: 32282285
J Neurosci. 2012 Sep 5;32(36):12411-20
pubmed: 22956832
Hum Factors. 2012 Oct;54(5):747-61
pubmed: 23156620
Ergonomics. 1992 Oct;35(10):1243-70
pubmed: 1516577
Appl Ergon. 2018 Oct;72:37-47
pubmed: 29885726
Injury. 2013 Dec;44 Suppl 4:S49-56
pubmed: 24377780
Clin Neurophysiol. 2012 Jul;123(7):1291-9
pubmed: 22154302
IEEE Trans Neural Syst Rehabil Eng. 2020 Aug;28(8):1790-1799
pubmed: 32746289
J Safety Res. 2020 Dec;75:262-274
pubmed: 33334485
Hum Factors. 2016 Mar;58(2):229-41
pubmed: 26646299
Hum Factors. 2011 Oct;53(5):517-27
pubmed: 22046724
Behav Brain Res. 2015 Oct 1;292:167-73
pubmed: 26068585