VTechWorks staff will be away for the Independence Day holiday from July 4-7. We will respond to email inquiries on Monday, July 8. Thank you for your patience.
 

Identifying Deviations from Normal Driving Behavior

Simple item page
dc.contributor.authorAlambeigi, Hananehen
dc.contributor.authorMcDonald, Anthony D.en
dc.contributor.authorShipp, Evaen
dc.contributor.authorManser, Michaelen
dc.date.accessioned2022-07-14T18:38:37Zen
dc.date.available2022-07-14T18:38:37Zen
dc.date.issued2022-01en
dc.description.abstractOne of the critical circumstances in automated vehicle driving is transition of control between partially automated vehicles and drivers. One approach to enhancing the design of transition of control is to predict driver behavior during a takeover by analyzing a driver’s state before the takeover occurs. Although there is a wealth of existing driver behavior model prediction literature, little is known regarding takeover performance prediction (e.g., driver error) and its underlying data structure (e.g., window size). Thus, the goal of this study is to predict driver error after a takeover event using supervised machine learning algorithms on various window sizes. Three machine learning algorithms—decision tree, random forest, and support vector machine with a radial basis kernel—were applied to driving performance, physiological, and glance data from a driving simulator experiment examining automated vehicle driving. The results showed that a random forest algorithm with an area under the receiver operating curve of 0.72, trained on a 3 s window before the takeover time, had the highest performance for accurately classifying driver error. In addition, we identified the 10 most critical predictors that resulted in the best error prediction performance. The results of this study can be beneficial for developing driver state algorithms that could be integrated into automated driving systems.en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/111253en
dc.language.isoenen
dc.publisherSAFE-D: Safety Through Disruption National University Transportation Centeren
dc.relation.ispartofseriesSafe-D;TTI-Student-08en
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectdriver behavioren
dc.subjectautomated drivingen
dc.subjecttransfer of controlen
dc.subjectmachine learning algorithmen
dc.subjectphysiological measuresen
dc.subjectpredictive modelingen
dc.titleIdentifying Deviations from Normal Driving Behavioren
dc.typeReporten
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TTI-Student-08-Final-Report-Clean8_edits-and-comments-from-VTTI-ready-for-site-NOw.pdf
Size:
1.56 MB
Format:
Adobe Portable Document Format
Description:
Report
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Safety through Disruption (SAFE-D) University Transportation Center (UTC)