The complexity to handle complex situations in automated driving requires increasing computational resources. In this work, we propose a machine learning approach for motion planning aiming at optimizing the set of path candidates to be evaluated in accordance with the driving context. Thus, the computation cost of the whole motion planning strategy can be reduced while generating safe and comfortable trajectories when required. The proposed strategy has been implemented in a real experimental platform and validated in different operating environments, successfully providing high quality trajectories in a small time frame. ; This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities with National Project COGDRIVE (DPI2017- 86915-C3-1-R), the Community of Madrid through SEGVAUTO 4.0-CM (S2018-EMT-4362) Programme, and by the European Commission through the Project PRYSTINE (ECSEL-783190-2). ; Peer reviewed


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    Title :

    Machine learning based motion planning approach for intelligent vehicles



    Publication date :

    2020-10-01


    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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