Skip to Main content Skip to Navigation
Conference papers

Trajectory prediction : a functional regression approach

Abstract : To solve the problem of trajectory prediction, traditional approaches can be classified into three categories : learning algorithms, nonparametric algorithms, simulation algorithms. The approach of this paper is to use the functional regression for the trajectory prediction, ie a method between learning algorithms and nonparametric methods. The proposed approach consists in the optimal decomposition of trajectories on a functional Karhunen Loève base and to learn the weights with a large set of registered trajectories. Those weights are also functions. Based on this optimal decomposition, generalization process enables to predict the shape of the trajectory in the near future. This method has been applied on real aircraft trajectories with successful results.
Document type :
Conference papers
Complete list of metadata
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Tuesday, July 1, 2014 - 5:13:02 PM
Last modification on : Tuesday, October 19, 2021 - 11:02:49 AM


  • HAL Id : hal-01004152, version 1



Daniel Delahaye, Stéphane Puechmorel, Loïc Boussouf. Trajectory prediction : a functional regression approach. ICRAT 2008, 3rd International Conference on Research in Air Transportation, Jun 2008, Fairfax, United States. pp xxxx. ⟨hal-01004152⟩



Record views