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  • 1.
    Jachimczyk, Bartosz
    et al.
    BetterSolutions S.A., POL.
    Dziak, Damian
    Politechnika Gdanska, POL.
    Czapla, Jacek
    Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
    Damps, Pawel
    Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
    Kulesza, Wlodek
    Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
    IoT on-board system for driving style assessment2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 4, article id 1233Article in journal (Refereed)
    Abstract [en]

    The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to consider a limited number of factors and indicators. This article addresses the problem of the objective assessment of driving style independent of circumstances. The proposed objective assessment of driving style is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy, and comfort. The presented solution is based on the embedded system designed according to the Internet of Things concept. The useful data are acquired from the car diagnostic port—OBD-II—and from an additional accelerometer sensor and GPS module. The proposed driving skills assessment method has been implemented and experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system was verified on long-route tests for analysis and could then improve the driver’s behavior behind the wheel. Moreover, the spider diagram approach that was used established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

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