Machine Learning Techniques to Identify Mind-Wandering and Predict Hazard Response Time in Fully Immersive Driving Simulation

dc.contributor.advisorHamilton-Wright, Andrew
dc.contributor.advisorTrick, Lana
dc.contributor.authorBeninger, John
dc.date.accessioned2020-08-25T20:16:49Z
dc.date.available2020-08-25T20:16:49Z
dc.date.copyright2020-07
dc.date.created2020-08-12
dc.date.issued2020-08-05
dc.degree.departmentSchool of Computer Scienceen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeComputer Scienceen_US
dc.description.abstractThis work presents machine learning based techniques for detecting mind-wandering and predicting hazard response time in driving using only easily measurable driving performance data (speed, horizontal and frontal acceleration, lane gap, and brake pressure). Such predictors are relevant as research tools in the driving simulation community. We present a simple method, and a feature extraction based method, of representing time-series driving performance data that both support machine learning based predictions. We use the two types of representations to compare the effectiveness of support vector machines, random forest, and multi-layer perceptrons on data from 117 drives performed by 39 participants during a previous study in the high-fidelity driving simulator at the University of Guelph. Classification of mind-wandering and prediction of hazard response time was successful when compared to baseline measures. Specifically, random forest methods were most effective in both types of prediction and feature extraction supported the strongest random forest prediction of hazard response time. A discussion of the reasoning for this is included. To our knowledge this is the first driving pattern based classification of mind-wandering in a fully immersive driving simulator.en_US
dc.description.sponsorshipOntario Ministry of Transportation
dc.description.sponsorshipCanada Foundation for Innovation
dc.description.sponsorshipOntario Research Fund
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada
dc.identifier.citationBeninger, J., Hamilton-Wright, A., Walker, H. E., & Trick, L. M. (2020). Machine learning techniques to identify mind-wandering and predict hazard response time in fully immersive driving simulation. Soft Computing, 1-9. https://doi.org/10.1007/s00500-020-05217-8
dc.identifier.urihttps://hdl.handle.net/10214/21119
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectDriving Simulationen_US
dc.subjectMind-Wanderingen_US
dc.subjectMachine Learningen_US
dc.subjectAttentionen_US
dc.subjectHazard Response Timeen_US
dc.titleMachine Learning Techniques to Identify Mind-Wandering and Predict Hazard Response Time in Fully Immersive Driving Simulationen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Beninger_John_202008_Msc.pdf
Size:
1.44 MB
Format:
Adobe Portable Document Format
Description: