Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study.

driver acceptance driver distraction haptic shared control lane keeping truck driving

Journal

Human factors
ISSN: 1547-8181
Titre abrégé: Hum Factors
Pays: United States
ID NLM: 0374660

Informations de publication

Date de publication:
12 2021
Historique:
pubmed: 20 6 2020
medline: 11 3 2022
entrez: 20 6 2020
Statut: ppublish

Résumé

This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

Sections du résumé

OBJECTIVE
This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving.
BACKGROUND
Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving.
METHOD
Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task.
RESULTS
Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB.
CONCLUSION
The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects.
APPLICATION
The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

Identifiants

pubmed: 32551951
doi: 10.1177/0018720820928622
pmc: PMC8593280
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

1380-1395

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Auteurs

Jeroen Roozendaal (J)

Delft University of Technology, The Netherlands.

Emma Johansson (E)

101097Volvo Group Trucks Technology, Gothenburg, Sweden.

Joost de Winter (J)

Delft University of Technology, The Netherlands.

David Abbink (D)

Delft University of Technology, The Netherlands.

Sebastiaan Petermeijer (S)

Delft University of Technology, The Netherlands.

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Classifications MeSH