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Exploratory Development of Algorithms for Determining Driver Attention Status

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dc.contributor.authorHerbers, Eileenen
dc.contributor.authorMiller, Martyen
dc.contributor.authorNeurauter, Lukeen
dc.contributor.authorWalters, Jacoben
dc.contributor.authorGlaser, Danielen
dc.date.accessioned2023-09-26T19:20:04Zen
dc.date.available2023-09-26T19:20:04Zen
dc.date.issued2023-09en
dc.date.updated2023-09-26T19:12:41Zen
dc.description.abstractObjective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS). Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state. Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof. Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased. Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers. Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1177/00187208231198932en
dc.identifier.eissn1547-8181en
dc.identifier.issn0018-7208en
dc.identifier.orcidHerbers, Eileen [0000-0002-5055-7777]en
dc.identifier.pmid37732402en
dc.identifier.urihttp://hdl.handle.net/10919/116338en
dc.language.isoenen
dc.publisherSAGEen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/37732402en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectautomationen
dc.subjectautonomous drivingen
dc.subjectcognitionen
dc.subjectdistractionen
dc.subjectdistraction and interruptionsen
dc.subjectdriver behavioren
dc.subjectexpert systemsen
dc.subjecteye movementsen
dc.subjectmotor behavioren
dc.subjectsurface transportationen
dc.subjecttrackingen
dc.subjecttrust in automationen
dc.subjectvehicle automationen
dc.subject40 Engineeringen
dc.subject42 Health sciencesen
dc.subject52 Psychologyen
dc.titleExploratory Development of Algorithms for Determining Driver Attention Statusen
dc.title.serialHuman Factorsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Graduate studentsen
pubs.organisational-group/Virginia Tech/Graduate students/Doctoral studentsen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Virginia Tech Transportation Instituteen

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