Localization is still one of the most challenging tasks in autonomous driving on city roads. Further development and improvement of automatic functions of vehicles in urban conditions are not possible without overcoming the problem of significant degradation of GNSS signal quality. The proposed approach to localization can provide information about vehicle position on the road in different operational conditions. Desired stability and quality are achieved by using the combination of conventional computer vision, neural networks and Kalman filtering.


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    Title :

    Robust Localization of a Self-Driving Vehicle in a Lane




    Publication date :

    2020-09-01


    Size :

    1641078 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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