In this paper, a prediction- and cost function-based algorithm (PCB) is proposed to implement robust freeway driving in autonomous vehicles. A prediction engine is built to predict the future microscopic traffic scenarios. With the help of a human-understandable and representative cost function library, the predicted traffic scenarios are evaluated and the best control strategy is selected based on the lowest cost. The prediction- and cost function-based algorithm is verified using the simulator of the autonomous vehicle Boss from the DARPA Urban Challenge 2007. The results of both case tests and statistical tests using PCB show enhanced performance of the autonomous vehicle in performing distance keeping, lane selecting and merging on freeways.


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    Title :

    A prediction- and cost function-based algorithm for robust autonomous freeway driving


    Contributors:
    Junqing Wei, (author) / Dolan, John M (author) / Litkouhi, B (author)


    Publication date :

    2010-06-01


    Size :

    856755 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Prediction and Cost Function-Based Algorithm for Robust Autonomous Freeway Driving, pp. 512-517

    Wei, J. / Dolan, J. / Litkouhi, B. et al. | British Library Conference Proceedings | 2010


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