This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.


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    Title :

    Imitation learning for vision-based lane keeping assistance




    Publication date :

    2017-10-01


    Size :

    241850 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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