Driver’s Assessment Using Smartphones
| Vol-4 | Issue-01 | January-2019 | Published Online: 10 January 2019 PDF ( 592 KB ) | ||
| Author(s) | ||
| Reeba Benny 1; Shweta Gawahale 2; Shivraj Jadhav 3; Rohan Kanade 4; Prof. Ajitkumar Shitole 5 | ||
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1Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune (India) 2Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune (India) 3Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune (India) 4Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune (India) 5Department of Computer Engineering, Hope Foundation’s International Institute of Information Technology, Pune (India) |
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| Abstract | ||
The mishaps caused by improper and rash driving on the roads is increasing day by day, thus causing a threat to many lives. A way to avoid these accidents caused by harsh driving is addressed in this paper which is, to give a driving license only to those drivers who drive in a professional manner. Assessing the driving style of a driver during their learning phase will give information about whether they are a good driver or not, and if they are reliable enough to drive in a proper manner. This method can be helpful in reducing the number of rash drivers and eliminate any false means used to gain a driving license. The RTO can use this method to assess the driving style of an individual acquiring a license to prove their eligibility. This paper gives us information about checking the authorization of user and also how the driving styles of an authorised user is compared to the standards that are set by the RTO to be eligible for a license. It concludes with a discussions of the challenges and any future works that would bring the proposed technique in practical use. |
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| Keywords | ||
| Smartphones, Accelerometer, KMP, Driving skill characterization, pattern recognition, image processing, machine learning, License generation | ||
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