A solution to autonomous lateral vehicle guidance using a neurocontroller that can learn from measured human-driving data without knowledge of the physical car parameters is discussed. Simulations and practical tests confirm that a small-size feedforward autonomous neural network (21 neurons) can learn to steer a vehicle at high speeds only from looking at human-driving examples. In this way, the network learns the total closed-loop behavior, including the nonlinear dynamics of the vehicle and the driver's individual driving style. The main result of practical investigations is that the neutral controller trained on human-driving examples exhibits an aperiodic behavior that does not vanish at higher speeds (tests performed up to 130 km/h) and produces fewer lateral deviations than the linear state controller.


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    Title :

    Neurocontrol for lateral vehicle guidance


    Additional title:

    Neurosteuerung zur seitlichen Fahrzeugführung


    Contributors:
    Neusser, S. (author) / Nijhuis, J. (author) / Spaanenburg, L. (author) / Hoefflinger, B. (author) / Franke, U. (author) / Fritz, H. (author)

    Published in:

    IEEE Micro ; 13 , 1 ; 57-66


    Publication date :

    1993


    Size :

    10 Seiten, 13 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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