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Méthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs

Abstract : With the democratization of connected devices equipped with GPS receivers, large quantities of vehicle trajectories become available, particularly via professional vehicle fleets, mobile navigation and collaborative driving applications. Recently, map inference techniques, aiming at deriving mapping information from these GPS tracks, have tended to complete or even replace traditional techniques. Initially restricted to the construction of road geometry, they are gradually being used to enrich existing networks, and in particular to build a digital database of road signs. Detailed and exhaustive knowledge of the infrastructure is an essential prerequisite in many areas : for network managers and decision-makers, for users with precise calculation of travel times, but also in the context of the autonomous vehicle, with the construction and updating of a high definition map providing in real time electronic horizons, which can supplement the system in the event of failures of the main sensors. In this context, statistical learning methods (e.g. Bayesian methods, random tree forests, neural networks,...) provide an interesting perspective and guarantee the adaptability of the approach to different use cases and the great variability of the data encountered in practice.In this thesis, we investigate the potential of this class of methods, for the automatic detection of traffic signals (mainly traffic lights), from a set of GPS speed profiles. First, we are working on an experimental, high-quality dataset, for which we compare the performances of several classifiers on classical image recognition approaches and on a functional approaches stemming from the field of signal processing, aggregating and decomposing speed profiles on a Haar wavelet basis whose coefficients are used as explanatory variables. The results obtained show the relevance of the functional approach, particularly when combined with the random forest algorithm, in terms of accuracy and computation time. The approach is then applied to other types of road signs.In a second part, we try to adapt the proposed method on the case of observational data for which we also try to estimate the position of the traffic lights by regression. The results show the sensitivity of the learning approach to the data noise and the difficulty of defining the spatial extent of individual instances on a complex road network. We are trying to solvethis second issue using global image approaches based on a segmentation by convolutional neural network, allowing us to avoid the definition of instances. Finally, we are experimenting an approach leveraging spatial autocorrelation of individual instances using the graph topology, by modeling the study area as a conditional Markov field. The results obtained show an improvement compared to the performance obtained with non-structured learning.This thesis work has also led to the development of original methods for pre-processing GPS trajectory data, in particular for filtering, debiaising coordinates and map-matching traces on a reference road network
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Yann Meneroux. Méthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs. Technologies Émergeantes [cs.ET]. Université Paris-Est, 2019. Français. ⟨NNT : 2019PESC2061⟩. ⟨tel-02493936⟩

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