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Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data / Citlalli Gamez Serna (2019)  

Public Titre : Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data Type de document : Thèse/HDR Auteurs : Citlalli Gamez Serna, Auteur ; Yassine Ruichek, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2019 Importance : 178 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université Bourgogne Franche-Comté préparée à l'Université de Technologie de Belfort-Montbéliard, Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] instance [Termes IGN] milieu urbain [Termes IGN] navigation autonome [Termes IGN] récepteur GPS [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] signalisation routière [Termes IGN] système de transport intelligent [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] véhicule sans pilote [Termes IGN] vision par ordinateur [Termes IGN] vision stéréoscopique [Termes IGN] vitesse Mots-clés libres : suivi d'itinéraire Index. décimale : THESE Thèses et HDR Résumé : (auteur) This PhD thesis focuses on developing a path tracking approach based on visual perception and localization in urban environments. The proposed approach comprises two systems. The first one concerns environment perception. This task is carried out using deep learning techniques to automatically extract 2D visual features and use them to learn in order to distinguish the different objects in the driving scenarios. Three deep learning techniques are adopted: semantic segmentation to assign each image pixel to a class, instance segmentation to identify separated instances of the same class and, image classification to further recognize the specific labels of the instances. Here our system segments 15 object classes and performs traffic sign recognition. The second system refers to path tracking. In order to follow a path, the equipped vehicle first travels and records the route with a stereo vision system and a GPS receiver (learning step). The proposed system analyses off-line the GPS path and identifies exactly the locations of dangerous (sharp) curves and speed limits. Later after the vehicle is able to localize itself, the vehicle control module together with our speed negotiation algorithm, takes into account the information extracted and computes the ideal speed to execute. Through experimental results of both systems, we prove that, the first one is capable to detect and recognize precisely objects of interest in urban scenarios, while the path tracking one reduces significantly the lateral errors between the learned and traveled path. We argue that the fusion of both systems will ameliorate the tracking approach for preventing accidents or implementing autonomous driving. Note de contenu : I- Context and problems 1- Introduction II- Contribution 2- Proposed datasets 3- Traffic sign classification 4- Visual perception system for urban environments 5- Dynamic speed adaptation system for path tracking based on curvature information and speed limits III- Conclusions and future works 6- Conclusions and future works Numéro de notice : 25967 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : UBFC : 2019 Organisme de stage : CIAD Dijon nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02160966/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96587