Autonomous vehicles (AVs) have been increasingly adopted for their potential to improve overall road safety and reduce traffic congestion. However, many significant challenges remain to be overcome before AVs can safely navigate busy urban environments, such as accurately perceiving their surroundings to avoid potential collisions. One of the most major tasks that provide the most value, and have a profound impact on AVs perception system progress is detecting pedestrians and vehicles, both in day and night conditions. This chapter reviews the recent advances in computer vision research, also known as vision-based techniques which use visual feedback information extracted from a vision sensor to detect and recognize pedestrians and vehicles, and even to control the motion of a vehicle, in an AV perception system. Traditional and deep learning (DL) methods are discussed in detail, highlighting their advantages and limitations in both day and night conditions. The review concludes that DL techniques provide the best results for the detection of pedestrians and vehicles, although small, occluded, and truncated objects are still hard to detect. Further research is recommended on new and challenging datasets, to attain the utmost performance of the algorithms in highly uncertain environments understanding, like in dense surroundings and difficult light and weather conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Driving Cars Perception Pedestrian Detection


    Weitere Titelangaben:

    Advances in Science, Technology & Innovation


    Beteiligte:
    Bendaoud, Mohamed (Herausgeber:in) / El Fathi, Amine (Herausgeber:in) / Bakhsh, Farhad Ilahi (Herausgeber:in) / Pierluigi, Siano (Herausgeber:in) / Ihssane, Bouasria (Autor:in) / Walid, Jebrane (Autor:in) / Nabil, El Akchioui (Autor:in)

    Kongress:

    International Conference on Electrical Systems & Automation ; 2023 ; Al Hoceïma, Morocco May 29, 2023 - May 30, 2023



    Erscheinungsdatum :

    01.06.2024


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    PEDESTRIAN SAFETY SYSTEM FOR SELF DRIVING CARS

    PARK SUNGHYEON / AN JIWON / JEONG JAEUK et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    PEDESTRIAN ALERT SYSTEM FOR SELF DRIVING CARS

    YU SUNG WON / KIM WOOJIN / JANG HEESEUNG et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    PEDESTRIAN WARNING SYSTEM FOR SELF DRIVING CARS

    HWANG YOUNHA / KIM SEUNGJU / PARK SEJUN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    PEDESTRIAN ADVERTISING SYSTEM FOR SELF DRIVING CARS

    PARK JIEUN / KIM MINJI / ZHANG HUIYAN et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    PEDESTRIAN SAFETY SYSTEM FOR SELF DRIVING CARS

    PARK YEDAM / KIM YEJIN / CHO YEON SIK et al. | Europäisches Patentamt | 2022

    Freier Zugriff