A framework for the discretization of navigable space within and around a cross intersection is proposed in this paper. The purpose of our approach is to capture the manner in which drivers manoeuvre in an intersection in order to facilitate and understand the decision-making tasks. Gaussian processes are used to learn and predict the most likely trajectories taken by multiple drivers in different situations. The merging and crossing areas are found by searching for the overlap between two predicted trajectories, whereas the area approaching the intersection is discretized by using the most probable occupancy. The generated areas are stored in the vehicle navigation map. The correlation between the proposed discretization and the driver's behaviour is demonstrated. The proposed framework enables also the discretization of the vehicle velocity profile, information that can be used to govern the decision-making function.


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    Title :

    Functional discretization of space using Gaussian processes for road intersection crossing


    Contributors:


    Publication date :

    2016-11-01


    Size :

    1246144 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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