Intelligent Transportation System (ITS) aims at improving the safety on roads, and with the advancement in vehicular technology, autonomous vehicles will soon be equally present on roads along with manual vehicles. Vehicle classification is used by autonomous vehicles to decide driving behavior. In this paper, we use transfer learning in deep learning for vehicle classification. Different algorithms are implemented and compared using Kaggle dataset. It is evident from the experimental results that the hybrid InceptionResNet algorithm exhibits the highest training accuracy of 92 percent and validation accuracy of 62 percent. Similarly, the validation of the InceptionResNet model results in 85 percent AUC. The classification results can be used by autonomous vehicles to improve driving behavior like lane changing, overtaking, etc., and improve the safety of the commute.


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    Titel :

    Vehicle Classification for Autonomous Vehicles Using Transfer Deep Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Rani, Asha (Herausgeber:in) / Kumar, Bhavnesh (Herausgeber:in) / Shrivastava, Vivek (Herausgeber:in) / Bansal, Ramesh C. (Herausgeber:in) / Chhabra, Rishu (Autor:in) / Khullar, Vikas (Autor:in) / Singh, Saravjeet (Autor:in)

    Kongress:

    International Conference on Signals, Machines, and Automation ; 2022 ; Delhi, India August 05, 2022 - August 06, 2022



    Erscheinungsdatum :

    23.05.2023


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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