Road departure prevention systems (RDPSs) for mitigating/avoiding road departure crashes have been developed and equipped in some high-end production vehicles in recent years. In order to provide a standardized and objective performance evaluation of RDPSs, this paper describes the test scenario development and the associated data acquisition and data postprocessing systems. Seven key variables are identified and analyzed, and their possible values are used to describe the most representative road departure test scenarios on both straight roads and curved roads. The overall structure and components of data collection and postprocessing systems for RDPSs$^\prime$ evaluation are devised and presented. The algorithms for computing vehicle dynamic information are developed. Experiments are performed on the test track under various scenarios. The results show that the sensing system and data postprocessing system can capture all necessary signals accurately and display the test vehicle motion profile effectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Test Scenarios Development and Data Collection Methods for the Evaluation of Vehicle Road Departure Prevention Systems


    Contributors:
    Shen, Dan (author) / Yi, Qiang (author) / Li, Lingxi (author) / Tian, Renran (author) / Chien, Stanley (author) / Chen, Yaobin (author) / Sherony, Rini (author)

    Published in:

    Publication date :

    2019-09-01


    Size :

    7623123 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    DATA COLLECTION AND PROCESSING METHODS FOR THE EVALUATION OF VEHICLE ROAD DEPARTURE DETECTION SYSTEMS

    Shen, Dan / Yi, Qiang / Li, Lingxi et al. | British Library Conference Proceedings | 2018





    Vehicle optimal road departure prevention via model predictive control

    Yuan, Hongliang / Gao, Yangyan / Gordon, Timothy J | SAGE Publications | 2017