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    Implementation of a fuzzy-inference-based, low-speed, close-range collision-warning system for urban areas

    Implementation of a fuzzy-inference-based, low-speed, close-range collision-warning system for urban areas


    Traffic accidents are still increasing even though vehicles are becoming more intelligent to enhance driver convenience and safety. Single car-on-car rear impacts in urban areas have increased rapidly due to driver inattention. According to a Road Traffic Authority (ROTA) report in Korea in 2006, 85.2% of single car-on-car rear impact accidents occurred at less than 60 km/h, and 25.3% of the total occurred at between 30 km/h and 50 km/h. To prevent rear vehicle crashes in urban areas, automobile manufacturers have developed various low-speed, close-range collision-warning systems. This paper presents a low-speed, close-range collision-warning algorithm for urban areas using fuzzy inference. Experiments using an embedded microprocessor in the driving track demonstrated the feasibility of the proposed collision-warning system. The results indicate that the fuzzy inference-based, low-speed, close-range collision-warning system could reduce traffic accidents by alerting the driver to potential collisions.

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    Title :

    Implementation of a fuzzy-inference-based, low-speed, close-range collision-warning system for urban areas

    Contributors:
    Kim, Man-Ho (author) / Lee, Suk (author) / Ha, Kyoung-Nam (author) / Lee, Kyung-Chang (author)
    Published in:

    Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ; 227 , 2 ; 234-245

    Publication date :

    2013-02-01

    Size :

    12 pages

    ISSN :

    0954-4070 , 2041-2991

    DOI :

    https://doi.org/10.1177/0954407012451724

    Type of media :

    Article (Journal)

    Type of material :

    Electronic Resource

    Language :

    English

    Keywords :

    single car-on-car rear impact traffic accident , low-speed close-range collision warning , sensor fusion , Kalman filter , Intelligent vehicle , fuzzy inference

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