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Functional Discretization of Space Using Gaussian Processes for Road Intersection

Abstract : This paper propose a new framework to discretize the space within and around a cross intersection. The main 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 a map. It was possible to show the correlation between this discretization and the drivers’ behaviour by looking at how the proposed framework also discretizes the velocity profile, which can then be applied to decision making.
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Submitted on : Saturday, December 17, 2016 - 2:03:39 PM
Last modification on : Thursday, January 20, 2022 - 4:14:45 PM
Long-term archiving on: : Tuesday, March 21, 2017 - 6:24:50 AM


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  • HAL Id : hal-01362223, version 1



Mathieu Barbier, Christian Laugier, Olivier Simonin, Javier Ibanez-Guzman. Functional Discretization of Space Using Gaussian Processes for Road Intersection. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016), IEEE Intelligent Transportation Systems Society, Nov 2016, Rio de Janeiro, Brazil. pp.7. ⟨hal-01362223⟩



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