The history of transportation spanning from 4000 BC to the present, is a mesmerizing subject that can deepen our understanding of travel from horses and camels, fixed-wheeled carts, riverboats, chariots, cycles, trains, airplanes, cars, and the development of the road locomotive. The future of transport is all about driverless cars or autonomous vehicles. The most crucial requirement for autonomous vehicles for navigation is to detect objects on the road. Autonomous vehicles provide potential answers for traffic congestion, road safety issues, and passenger comfort. The proposed model can detect and classify objects for assisting autonomous driving vehicles with the use of deep learning and neural network-based learning approaches. It aims to segment objects like people and automobiles through the Xception model, which carries out semantic segmentation, and Mask RCNN approaches, for instance, segmentation. Both these methods exhibit improvements in detection and are effective and relatively accurate with the results compared to Deep neural networks (DNN) in Contrast to Mask R-CNN and Xception model, respectively.


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    Titel :

    Exploiting the Joint Potential of Instance Segmentation and Semantic Segmentation in Autonomous Driving


    Beteiligte:


    Erscheinungsdatum :

    24.01.2023


    Format / Umfang :

    2331947 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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