Artificial Intelligence (A.I.) and Big Data are increasing become more applicable in the development of technology from machine design and mobility to bio-printing and drug discovery. The ability to quantify large amounts of data these systems generate will be paramount to establishing a robust infrastructure for interdisciplinary autonomous applications. This paper purposes an integrated approach to the environment, pre/post data processing, integration, and system security for robust systems in intelligent transportation systems. The systems integration is based on a FPGA embedded system design and computing (EDGE) platform utilizing image processing CNN algorithms from High Energy Physics (HEP) experiments in data centers with associative memory to ROS- FPGA technology in vehicles for hyper-scale infrastructure scalability. The ability to process data in the future is equivalent to collision particle detection that the Large Hadron Collider (LHC) produces at CERN. The future of robust scalability will depend upon how seamlessly several applications can be integrated into a high-performance package with minimal consumption. The proposed architecture will entirely be dependent on a digital network with special attention paid to costs and power consumption needed for a single server platform. In Sect. II, a background of autonomous vehicles, industry analysis, trucking industry shortage, and the automotive industry’s race for driverless are provided. Sect. III, outlines the current levels of autonomous and CERN’s ability to quantify large amounts of data. Sect. IV, proposes an integrated infrastructure addressing both hardware and interconnected network scalability. Lastly, Sect. V, provides a logic gate analysis, compilation, and simulation of 18 bit NOR gates for Content Addressable Memory (CAM) associative memory design for XOR CMOS used in the ATLAS detector at CERN.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    New Paradigm in Robust Infrastructure Scalability for Autonomous Applications


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    WCX SAE World Congress Experience ; 2019



    Publication date :

    2019-04-02




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    New Paradigm in Robust Infrastructure Scalability for Autonomous Applications

    Brown, Kyle W. | British Library Conference Proceedings | 2019


    Scalability Analysis of Infrastructure Networks for Vehicular Safety Applications

    Vinel, Alexey / Bellalta, Boris / Chilamkurti, Naveen et al. | IEEE | 2012


    Scoping Out the Scalability Issues of Autonomous Vehicle-Pedestrian Interaction

    Tran, Tram Thi Minh / Parker, Callum / Tomitsch, Martin | ArXiv | 2024

    Free access


    ROBUST TRACKING OF REFERENCE TRAJECTORIES FOR AUTONOMOUS DRIVING IN INTELLIGENT ROADSIDE INFRASTRUCTURE

    Fleck, Tobias / Ochs, Sven / Zofka, Marc Renè et al. | British Library Conference Proceedings | 2020