This paper presents a cost effective development platform, named FEV-Driver, for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). The FEV-Driver platform is an electric go-kart that was converted into an x-by-wire vehicle which represents the behavior of a full-scale electric vehicle. FEV-Driver has the advantage of being a small-scale vehicle that can be used with a significant lower safety risk compared to full-sized vehicles. The ADAS/AD algorithms for this platform were developed in both Simulink and C++ software and implemented within the Robot Operating System (ROS) middleware. Besides the description of the platform, Lane Keep Assist (LKA) and Automatic Emergency Braking (AEB) algorithms are discussed, followed by a path planning algorithm which enables the vehicle to drive autonomously after a manually controlled training lap. The modular system architecture allows for complete controller exchange or adaptation to different vehicles. The adaptation and implementation of the platform into a full-scale passenger vehicle is described in the last section of this paper. The presented platform has proven to be a low-cost scalable platform for development and verification of ADAS and AD functions.


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    Titel :

    Cost Effective Automotive Platform for ADAS and Autonomous Development


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Quail, Alanna (Autor:in) / Dwyer, Brian (Autor:in) / Alzu'bi, Hamzeh (Autor:in) / Pischinger, Martin (Autor:in) / Nagaraj, Sarika (Autor:in)

    Kongress:

    WCX World Congress Experience ; 2018



    Erscheinungsdatum :

    2018-04-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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