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    Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations

    Access Status
    Fulltext not available
    Authors
    Hammarstrand, L.
    Fatemi, M.
    Garcia Fernandez, Angel
    Svensson, L.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Hammarstrand, L. and Fatemi, M. and Garcia Fernandez, A. and Svensson, L. 2016. Long-Range Road Geometry Estimation Using Moving Vehicles and Roadside Observations. IEEE Transactions on Intelligent Transportation Systems. 17 (8): pp. 2144-2158.
    Source Title
    IEEE Transactions on Intelligent Transportation Systems
    DOI
    10.1109/TITS.2016.2517701
    ISSN
    1524-9050
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/54476
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents an algorithm for estimating the shape of the road ahead of a host vehicle equipped with the following onboard sensors: a camera, a radar, and vehicle internal sensors. The aim is to accurately describe the road geometry up to 200 m ahead in highway scenarios. This purpose is accomplished by deriving a precise clothoid-based road model for which we design a Bayesian fusion framework. Using this framework, the road geometry is estimated using sensor observations on the shape of the lane markings, the heading of leading vehicles, and the position of roadside radar reflectors. The evaluation on sensor data shows that the proposed algorithm is capable of capturing the shape of the road well, even in challenging mountainous highways.

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