Road surface anomalies are a serious issue for safe and effective traffic flow; they are becoming more common due to factors like poor materials for construction, heavy traffic, climate change, and high traffic numbers. The vehicular ad hoc network (VANET), a developing research area, provides the network's vehicles with essential data. Finding and fixing these defects is essential to the mechanical safety of drivers, passengers, and automobiles. Based on the integration of artificial intelligence into automobiles, autonomous cars may find it useful to use a combination of sensors and DNN techniques to sense their environment and identify tracks and obstacles for safe and easy navigation. One of the primary problems for autonomous vehicles is avoiding dangerous situations on the road caused by serious road defects. The Intelligent Transportation System (ITS) developed the concept of a vehicle network known as a "vehicular ad hoc network (VANET)" for assuring security and safety in a traffic flow in order to handle accident issues and relay emergency information. Using Edge AI and VANET, a novel method has been put proposed allowing autonomous automobiles to recognise defects on the road automatically and notify oncoming traffic of them. The methods "Residual Convolution Neural Network (ResNet-18) and Visual Geometry Group (VGG-16)" are used to automatically detect and classify the roads with irregularities, like a bump, crack or pothole, and plain roads with no anomaly using data from several online sources. The results show that the models that were utilized performed better at identifying road abnormalities than other methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automated Road anomaly detector in VANET by using Deep Learning


    Beteiligte:
    Bondre, Shweta V. (Autor:in) / Bondre, Vipin D. (Autor:in) / Yadav, Uma (Autor:in) / Thakare, Bhakti (Autor:in) / Nanwani, Jaya (Autor:in)


    Erscheinungsdatum :

    2023-12-13


    Format / Umfang :

    673230 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

    Rozi Bibi / Yousaf Saeed / Asim Zeb et al. | DOAJ | 2021

    Freier Zugriff

    Exploring Anomaly Detection Techniques for Enhancing VANET Availability

    Weber, Julia Silva / Ferreto, Tiago / Zincir-Heywood, Nur | IEEE | 2023


    Smart Traffic Monitoring and Alert System Using VANET and Deep Learning

    Taneja, Manik / Garg, Neeraj | Springer Verlag | 2021


    Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

    Muhammad Asim Saleem / Zhou Shijie / Muhammad Umer Sarwar et al. | DOAJ | 2021

    Freier Zugriff

    Exploring Realistic VANET Simulations for Anomaly Detection of DDoS Attacks

    Baharlouei, Hamideh / Makanju, Adetokunbo / Zincir-Heywood, Nur | IEEE | 2022