All drivers have their own habitual choice of driving behavior, causing variations in fuel consumption. It would be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a novel technique to robustly classify driving style using the Support Vector Clustering approach, which attempts to differentiate the variations in individual's driving pattern and provides an objective driver classification. Real driving data of three human participants were collected using an instrumented vehicle. For data processing, each trip data were first segmented into separate event groups. Prominent factors were then extracted by applying Principal Component Analysis on both statistical and spectral features of all signals. Afterwards, Support Vector Clustering was performed to classify driving style during the trip. The trained classifier was used to indicate the driving pattern variations in percentage. The validity of the proposed method was evaluated using the jerk profile, where a high correlation was found between the classification results and jerk distributions. Moreover, a positive relation between fuel consumption and driving aggressivity was also confirmed. Furthermore, it was found that weather condition, time of the day and ultimately, the driver's eagerness, can cause significant variations in driving style.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driving Style Analysis by Classifying Real-World Data with Support Vector Clustering


    Contributors:


    Publication date :

    2018-09-01


    Size :

    7809414 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driving Style Clustering using Naturalistic Driving Data

    Chen, Kuan-Ting / Chen, Huei-Yen Winnie | Transportation Research Record | 2019


    Driving Style Identification Algorithm with Real-World Data Based on Statistical Approach

    Ouali, Tarek / Shah, Nirav / Kim, Bill et al. | British Library Conference Proceedings | 2016


    Driving Style Identification Algorithm with Real-World Data Based on Statistical Approach

    Kim, Bill / Gao, Bo / Fuente, David et al. | SAE Technical Papers | 2016


    Driving Style Recognition Incorporating Risk Surrogate by Support Vector Machine

    Xue, Qingwen / Lu, Jianjohn / Gao, Kun | TIBKAT | 2021


    Driving Style Recognition Incorporating Risk Surrogate by Support Vector Machine

    Xue, Qingwen / Lu, Jianjohn / Gao, Kun | Springer Verlag | 2021