Amelio-rater: Detection and Classification of Driving Abnormal Behaviours for Automated Ratings and Real-Time Monitoring

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dc.contributor.author El Masry, Noha
dc.contributor.author El-Dorry, Passant
dc.contributor.author El Ashram, Mariam
dc.contributor.author Atia, Ayman
dc.contributor.author Tanaka, Jiro
dc.date.accessioned 2020-02-22T07:00:05Z
dc.date.available 2020-02-22T07:00:05Z
dc.date.issued 2018
dc.identifier.isbn 978-1-5386-5112-4
dc.identifier.other https://doi.org/10.1109/ICCES.2018.8639398
dc.identifier.uri https://cutt.ly/er0O0d4
dc.description MSA Google Scholar en_US
dc.description.abstract Real-time monitoring of the drivers may be a factor that would force them to drive safely. In this paper, we introduce a system named 'Amelio-Rater", that focuses on detection and classification of abnormal driving behaviours for automatically generating driver ratings and real-time monitoring. To reduce malicious ratings, the Amelio-rater introduces an automatic rating system which is calculated purely based on the driver's driving behaviours only. Each driver will be given his own Amelio-rater rate and a manual user rate. There are multiple types of driving abnormal behaviours monitored by the proposed system such as meandering, single weaves, sudden changing of lanes and speeding. The classification results achieved showed that the Amelio-rater reached an accuracy of 95%. Our experiments showed that the manual user rates given for the driving behaviour are not far from the rates given by Amelio-rater. Amelio-rater rates were very close to the actual rates given by the users en_US
dc.description.sponsorship IEEE en_US
dc.description.uri https://www.scimagojr.com/journalsearch.php?q=21100797225&tip=sid&clean=0
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries 13th International Conference on Computer Engineering and Systems (ICCES) 2018;
dc.subject October University for University for Roads en_US
dc.subject Time series analysis en_US
dc.subject Automobiles en_US
dc.subject Intelligent sensors en_US
dc.subject Smart phones en_US
dc.title Amelio-rater: Detection and Classification of Driving Abnormal Behaviours for Automated Ratings and Real-Time Monitoring en_US
dc.type Book chapter en_US
dc.identifier.doi https://doi.org/10.1109/ICCES.2018.8639398
dc.Affiliation October University for modern sciences and Arts (MSA)


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