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access icon free Curve speed model for driver assistance based on driving style classification

  • Author(s): Duanfeng Chu 1 ; Zejian Deng 1 ; Yi He 1 ; Chaozhong Wu 1 ; Chuan Sun 1 ; Zhenji Lu 2
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  • Source: Volume 11, Issue 8, October 2017, p. 501 – 510
    DOI:  10.1049/iet-its.2016.0294 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 14/11/2016, Accepted 12/07/2017, Revised 11/05/2017, Published 14/07/2017

Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle–road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K-means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors’ model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.

Inspec keywords: road safety; driver information systems; pattern clustering; behavioural sciences; pattern classification; alarm systems

Other keywords: fuzzy synthetic evaluation method; driver assistance; road factors; driving comfort; vehicle–road interaction model; driving style classification; driving safety; vehicle factors; curve speed model; lateral instability crashes; road adhesion coefficient; driver behaviour factor; curve speed warning system; K-means clustering method

Subjects: Data handling techniques; Social and behavioural sciences computing; Traffic engineering computing

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