This study illustrates a methodology to reduce the time and effort spent on full-scale Intelligent Transportation System testing, through the use of small-scale testbeds. Scaled down testing platforms enable the researchers to implement, compare, and assess different architectures for intelligent transportation by deploying hardware-in-the-loop (HIL) simulation and testing, giving strong indications on the performance and high-level behavior of such systems at full scale. The performance of the scaled down testing is illustrated using a specific example based on an autonomous parking. The approach is demonstrated on intelligent transportation system testbed in The Ohio State University Control and Intelligent Transportation Research Laboratory. The detailed experimental results show the applicability and robustness of the proposed system.


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    Title :

    Using scaled down testing to improve full scale intelligent transportation


    Contributors:


    Publication date :

    2014-06-01


    Size :

    4008637 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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