Advanced driver-assistance systems (ADAS) play a significant role in reducing traffic fatalities and vehicular accidents. Among the many ADAS technologies is the blind spot alert monitor alerts you of a vehicle or an obstruction before you change lanes. This paper investigates extending the monitoring concept to detect if a car emerging from the blind spot is attempting to cut into your lane. In that respect, we would perceive the direction of the infringing vehicle. There are various means of sensing such a situation, including radar, lidar, camera, and sonar. We focus on using lidar and deep learning for this purpose in this paper. In particular, we present the experiments carried out to verify the concept, including details of the equipment, experimental data, and MATLAB Deep Learning for detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-Based Vehicle Direction Detection


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Arai, Kohei (Herausgeber:in) / Sebi, Nashwan J. (Autor:in) / Kobayashi, Kazuyuki (Autor:in) / Cheok, Ka C. (Autor:in)

    Kongress:

    Proceedings of SAI Intelligent Systems Conference ; 2021 ; Amsterdam September 02, 2021 - September 03, 2021



    Erscheinungsdatum :

    07.08.2021


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Vehicle lane direction detection

    PEREZ BARRERA OSWALDO / JIMENEZ HERNANDEZ ALVARO / FERREIRA FRANCISCO JAVIER | Europäisches Patentamt | 2018

    Freier Zugriff

    VEHICLE LANE DIRECTION DETECTION

    PEREZ BARRERA OSWALDO / JIMENEZ HERNANDEZ ALVARO / FERREIRA FRANCISCO JAVIER | Europäisches Patentamt | 2018

    Freier Zugriff

    Vehicle lane direction detection

    OSWALDO PEREZ BARRERA / ALVARO JIMENEZ HERNANDEZ / FRANCISCO JAVIER FERREIRA | Europäisches Patentamt | 2018

    Freier Zugriff