Frequency and Quality of Exposure to Adaptive Cruise Control and Impact on Trust, Workload, and Mental Models.

Advanced Driver Assistance Systems Driver Workload Driving Simulation Exposure Longitudinal Study Mental Models Trust

Journal

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

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 01 03 2023
revised: 22 05 2023
accepted: 22 05 2023
medline: 10 7 2023
pubmed: 20 6 2023
entrez: 19 6 2023
Statut: ppublish

Résumé

Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS resulting in inadequate mental models. This may result in drivers misusing ADAS, or mistrusting the technologies, especially after encountering edge-case events (situations beyond the capability of an ADAS where the system may malfunction or fail) and may also adversely affect driver workload. Literature suggests mental models could be improved through exposure to ADAS-related driving situations, especially those related to ADAS capabilities and limitations. The objective of this study was to examine the impact of frequency and quality of exposure on drivers' understanding of Adaptive Cruise Control (ACC), their trust, and their workload after driving with ACC. Sixteen novice ACC users were recruited for this longitudinal driving simulator study. Drivers were randomly assigned to one of two groups - the 'Regular Exposure' group encountering 'routine' edge-case events, and the 'Enhanced Exposure' group encountering 'routine' and 'rare' events. Each participant undertook four different simulator sessions, each separated by about a week. Each session comprised a simulator drive featuring five edge-case scenarios. The study followed a mixed-subject design, with exposure frequency as the within-subject variable, and quality of exposure (defined by two groups) as the between-subject variable. Surveys measured drivers' trust, workload, and mental models. The results from the analyses using linear regression models revealed that drivers' mental models about ACC improve with frequency of exposure to ACC and associated edge-case driving situations. This was more the case for drivers who experienced 'rare' ACC edge cases. The findings also indicate that for those who encountered 'rare' edge cases, workload was higher and trust was lower than those who did not. These findings are significant since they underline the importance of experience and familiarity with ADAS for safe operation. While these findings indicate that drivers benefit from increased exposure to ACC and edge cases in terms of appropriate use of ADAS, and ultimately promise crash reductions and injury prevention, a challenge remains regarding how to provide drivers with appropriate exposure in a safe manner.

Identifiants

pubmed: 37336048
pii: S0001-4575(23)00177-X
doi: 10.1016/j.aap.2023.107130
pii:
doi:

Types de publication

Journal Article Randomized Controlled Trial

Langues

eng

Sous-ensembles de citation

IM

Pagination

107130

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

Ganesh Pai (G)

Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States. Electronic address: gpaimangalor@umass.edu.

Fangda Zhang (F)

The Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children's Hospital, 700 Children's Drive, RB3, Columbus, OH 43205, United States. Electronic address: Fangda.Zhang@nationwidechildrens.org.

Apoorva P Hungund (AP)

Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States. Electronic address: ahungund@umass.edu.

Jaji Pamarthi (J)

Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States. Electronic address: jpamarthi@umass.edu.

Shannon C Roberts (SC)

Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States. Electronic address: scroberts@umass.edu.

William J Horrey (WJ)

AAA Foundation for Traffic Safety, 607 14th Street NW, Suite 201, Washington, DC 20005, United States. Electronic address: WHorrey@aaafoundation.org.

Anuj K Pradhan (AK)

Department of Mechanical and Industrial Engineering, 160 Governors Drive, University of Massachusetts Amherst, Amherst, MA 01003, United States. Electronic address: anujkpradhan@umass.edu.

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