Driving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the improvement of fuel efficiency are essential goals of current research in driving style characterization. However, the comfort perspective has still hardly been investigated, despite its importance for the future of driving automation. This paper proposes a driving style classification method, focused on global comfort of the driver and the passengers, but which can also be integrated into the above safety-efficiency viewpoint. Although human comfort in vehicles is affected by different factors, the amplitude and frequency of accelerations are recognized as key signals for assessing driving comfort. The proposed DS classification approach is based on a hybrid machine learning method that combines an unsupervised clustering method with a data-driven extreme learning machine (ELM) algorithm. Hierarchical clustering is used to explore the relevance of the acceleration components in relation to ride comfort, while a single layer ELM topology is implemented to model the DS classifier. The method has been evaluated using experimental data obtained with an instrumented car equipped with in-vehicle sensors and measurement units. The obtained clustering results are consistent with comfort standard indicators, while the data-driven algorithm provides encouraging results: more than 95 % classification rate using unseen data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driving Style Recognition based on Ride Comfort Using a Hybrid Machine Learning Algorithm*


    Beteiligte:


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    440458 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An eco‐driving approach for ride comfort improvement

    Mata‐Carballeira, Ó. / del Campo, I. / Asua, E. | Wiley | 2022

    Freier Zugriff

    An eco‐driving approach for ride comfort improvement

    Ó. Mata‐Carballeira / I. del Campo / E. Asua | DOAJ | 2022

    Freier Zugriff

    An eco-driving approach for ride comfort improvement

    Mata-Carballeira, Óscar / del Campo, Inés / Asua, Estibalitz | ArXiv | 2025

    Freier Zugriff

    Ride Comfort Enhancement Using Deep Reinforcement Learning

    Khandavalli, Guru Bhargava / Eckstein, Lutz | TIBKAT | 2020


    Ride Comfort Measurements

    Fernandes, Claudio Gomes / Massa, Luiz Carlos / Sato, Roberto Kiyoshi | SAE Technical Papers | 2005