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Kernel Regression for Vehicle Trajectory Reconstruction under Speed and Inter-vehicular Distance Constraints

Abstract : This work tackles the problem of reconstructing vehicle trajectories with the side information of physical constraints, such as inter-vehicular distance and speed limits. It is notoriously difficult to perform a regression while enforcing these hard constraints on time intervals. Using reproducing kernel Hilbert spaces, we propose a convex reformulation which can be directly implemented in classical solvers such as CVXGEN. Numerical experiments on a simple dataset illustrate the efficiency of the method, especially with sparse and noisy data.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03021643
Contributor : François Chaplais <>
Submitted on : Tuesday, November 24, 2020 - 1:54:16 PM
Last modification on : Thursday, December 3, 2020 - 3:05:59 AM
Long-term archiving on: : Thursday, February 25, 2021 - 7:53:45 PM

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  • HAL Id : hal-03021643, version 1

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Pierre-Cyril Aubin-Frankowski, Nicolas Petit, Zoltán Szabó. Kernel Regression for Vehicle Trajectory Reconstruction under Speed and Inter-vehicular Distance Constraints. IFAC 2020 World Congress, Jul 2020, Virtuel, Germany. ⟨hal-03021643⟩

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