Driver distraction detection using machine learning algorithms – an experimental approach

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dc.contributor.authorZhang, Zhaozhong
dc.contributor.authorVelenis, Efstathios
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorCao, Dongpu
dc.date.accessioned2021-05-20T11:20:00Z
dc.date.available2021-05-20T11:20:00Z
dc.date.issued2021-05-08
dc.description.abstractDriver distraction is the leading cause of accidents that contributes to 25% of all road crashes. In order to reduce the risks posed by distraction, warning must be given to the driver once distraction is detected. According to the literature, no rankings of relevant features have been presented. In this study, the most relevant features in detecting driver distraction are identified in a closed testing environment. The relevant features are found to be the mean values of speed and lane deviation, maximum values of eye gaze in direction, and head movement in direction. After the relevant features have been identified, pre-processed data with relevant features are fed into decision tree classifiers to discriminate the data into normal and distracted driving. The results show that detection accuracy of 78.4% using decision tree can be achieved. By eliminating unhelpful features, the time required to process data is reduced by around 40% to make the proposed technique suitable for real-time application.en_UK
dc.identifier.citationZhang Z, Velenis E, Fotouhi A, et al., (2021) Driver distraction detection using machine learning algorithms – an experimental approach. International Journal of Vehicle Design, Volume 83, Issue 2-4, May 2021, pp. 122-139en_UK
dc.identifier.issn0143-3369
dc.identifier.urihttps://doi.org/10.1504/IJVD.2020.115057
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16699
dc.language.isoenen_UK
dc.publisherInderscienceen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDriver distractionen_UK
dc.subjectFeature extractionen_UK
dc.subjectMachine learningen_UK
dc.subjectDecision treeen_UK
dc.titleDriver distraction detection using machine learning algorithms – an experimental approachen_UK
dc.typeArticleen_UK

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