Car driving safety represents one of the major targets of the ADAS (Advanced Driver Assistance Systems) technologies deeply investigated by the scientific community and car makers. From intelligent suspension control systems to adaptive braking systems, the ADAS solutions allows to significantly improve both driving comfort and safety. The aim of this contribution is to propose a driving safety assessment system based on deep networks equipped with self-attention Criss-Cross mechanism to classify the driving road surface combined with a physio-based drowsiness monitoring of the driver. The retrieved driving safety assessment performance confirmed the effectiveness of the proposed pipeline.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Road Surface Deep Embedded Classifier for an Efficient Physio-Based Car Driver Assistance


    Contributors:
    Rundo, F. (author) / Leotta, R. (author) / Piuri, V. (author) / Genovese, A. (author) / Scotti, F. (author) / Battiato, S. (author)


    Publication date :

    2021-08-11


    Size :

    935420 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Driver Assistance and Road Safety

    Kühn, Matthias / Hannawald, Lars | Springer Verlag | 2015


    Lateral driving assistance using robust control and embedded driver-vehicle-road model

    Mammar, S. / Raharijaona, T. / Glaser, S. et al. | British Library Conference Proceedings | 2004


    Lateral driving assistance using robust control and embedded driver-vehicle-road model

    Mammar, Said / Raharijaona, Thibaut / Glaser, Sebastien et al. | Tema Archive | 2004


    Lateral driving assistance using robust control and embedded driver-vehicle-road model

    Mammar,S. / Raharijaona,T. / Glaser,S. et al. | Automotive engineering | 2004