This chapter reviews the latest progress in the design of edge computing systems for autonomous driving applications. It begins with benchmark suites available for evaluating edge computing system designs. The chapter then reviews different approaches in designing computer architectures for autonomous driving workloads. Next, it describes the designs of runtime layers for efficient mapping of incoming workloads onto heterogeneous computing units. The chapter also discusses the designs of middleware for binding different autonomous driving functional modules. The middleware layer facilitates the communication between different autonomous driving services. The chapter summarizes several challenges. First, the middleware should impose minimal computing overhead and memory footprint, thus making it scalable. Secondly, as some autonomous driving services may stay in edge clouds, the middleware should enable a smooth edge client and cloud communication. The chapter further presents real‐world implementations of autonomous driving edge computing systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Edge Computing for Autonomous Vehicles


    Contributors:


    Publication date :

    2020-04-13


    Size :

    11 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    ROADSIDE EDGE COMPUTING SYSTEM FOR AUTONOMOUS VEHICLES

    RAN BIN / AN BOCHENG / ZHOU ZHI et al. | European Patent Office | 2024

    Free access

    Pose Correction of Autonomous Vehicles with Edge Computing

    Zhu, Kongtao / Huang, Rongyao / Chen, Shitao et al. | IEEE | 2021


    DYNAMIC EDGE COMPUTING WITH RESOURCE ALLOCATION TARGETING AUTONOMOUS VEHICLES

    RAKSHIT SARBAJIT K / KARRI VENKATA VARA PRASAD / MOYAL SHAILENDRA et al. | European Patent Office | 2023

    Free access


    VECFrame: A Vehicular Edge Computing Framework for Connected Autonomous Vehicles

    Tang, Sihai / Chen, Bruce / Iwen, Harold et al. | IEEE | 2021