Driver response and recovery following automation initiated disengagement in real-world hands-free driving.

Automation disengagement driver behavior hands-free driving naturalistic data visual attention

Journal

Traffic injury prevention
ISSN: 1538-957X
Titre abrégé: Traffic Inj Prev
Pays: England
ID NLM: 101144385

Informations de publication

Date de publication:
2023
Historique:
medline: 17 4 2023
pubmed: 30 3 2023
entrez: 29 3 2023
Statut: ppublish

Résumé

Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers' behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver's being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers' response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors. Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC. Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50 Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers' search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Identifiants

pubmed: 36988583
doi: 10.1080/15389588.2023.2189990
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

356-361

Auteurs

Pnina Gershon (P)

Massachusetts Institute of Technology, Center for Transportation Logistics, AgeLab, Cambridge, Massachusetts.

Bruce Mehler (B)

Massachusetts Institute of Technology, Center for Transportation Logistics, AgeLab, Cambridge, Massachusetts.

Bryan Reimer (B)

Massachusetts Institute of Technology, Center for Transportation Logistics, AgeLab, Cambridge, Massachusetts.

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