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Title:
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CLASSIFICATION OF DRIVING TRAITS USING FUZZY-LOGIC CONTROL |
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Author(s):
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Dylan Seychell and Steven Farrugia |
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ISBN:
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978-989-8533-75-3 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2018 |
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Edition:
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Single |
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Keywords:
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Driver classification, Driving Behavior, Fuzzy logic inference |
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Type:
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Full Paper |
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First Page:
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201 |
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Last Page:
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208 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Younger as well as less experienced drivers have largely contributed to the alarmingly high vehicle collision rate globally. Improving smartphone and onboard technology can help to profile drivers and subsequently detect abnormal driving traits that can be potentially hazardous while providing an opportunity to reward good driving. This paper explores the relationship between driving traits and collision rates among different age groups and driving experience. This paper presents a solution that uses a smartphone application to classify drivers by using sensor data and feeding it into a fuzzy inference system, mapping acceleration and speed to the corresponding driving classification output through defined fuzzy logic rules. A survey with 806 respondents provided insights about drivers attitude towards such profiling with an encouraging 65% stating they are willing to provide their data in return for driving reports. The fuzzy inference system was then evaluated with 20 drivers of different age and experience. They were asked to follow a designated route while having the mobile application recording the journey. The system successfully profiled the drivers in relation to their driving record with the drivers classified as most aggressive by the system being the same drivers with most collisions in their driving history. |
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