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Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)  

Public Titre : Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles Type de document : Thèse/HDR Auteurs : Abdelhak Loukkal, Auteur ; Yves Grandvalet, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2021 Importance : 129 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse présentée pour l’obtention du grade de Docteur de l’UTC, spécialité Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] compréhension de l'image [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fusion de données multisource [Termes IGN] navigation autonome [Termes IGN] reconnaissance de formes [Termes IGN] réseau neuronal profond [Termes IGN] segmentation sémantique [Termes IGN] système de navigation [Termes IGN] véhicule automobile [Termes IGN] vision monoculaire [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (Auteur) During this thesis, some perception approaches for self-driving vehicles were developed using de convolutional neural networks applied to monocular camera images and High-Definition map (HD-ma rasterized images. We focused on camera-only solutions instead of leveraging sensor fusion with rang sensors because cameras are the most cost-effective and discrete sensors. The objective was also to show th camera-based approaches can perform at par with LiDAR-based solutions on certain 3D vision tasks. Rea world data was used for training and evaluation of the developed approaches but simulation was als leveraged when annotated data was lacking or for safety reasons when evaluating driving capabilities. Cameras provide visual information in a projective space where the perspective effect does not preserve th distances homogeneity. Scene understanding tasks such as semantic segmentation are then often operated i the camera-view space and then projected to 3D using a precise depth sensor such as a LiDAR. Having thi scene understanding in the 3D space is useful because the vehicles evolve in the 3D world and the navigatio algorithms reason in this space. Our focus was then to leverage the geometric knowledge about the camer parameters and its position in the 3D world to develop an approach that allows scene understanding in the 3D space using only a monocular image as input. Neural networks have also proven to be useful for more than just perception and are more and more used fo the navigation and planning tasks that build on the perception outputs. Being able to output 3D scen understanding information from a monocular camera has also allowed us to explore the possibility of havin an end-to-end holistic neural network that takes a camera image as input, extracts intermediate semantic information in the 3D space and then lans the vehicle's trajectory. Note de contenu : 1. Introduction 1.1 General context 1.2 Framework and objectives 1.3 Organization and contributions of the thesis 2. Background and related work 2.1 Introduction 2.2 Autonomous driving perception datasets 2.3 Autonomous driving simulators 2.4 Semantic segmentation with CNNs 2.5 Monocular depth estimation with CNNs 2.6 Driving with imitation learning 2.7 Conclusion 3. Semantic segmentation using cartographic and depth maps 3.1 Introduction 3.2 Synthetic dataset 3.3 Proposed methods 3.4 Experiments 3.5 Conclusion 4. Disparity weighted loss for semantic segmentation 4.1 Introduction 4.2 Disparity weighting for semantic segmentation 4.3 Experiments 4.4 Conclusion 5. FlatMobileNet: Bird-Eye-View semantic masks from a monoc?ular camera 5.1 Introduction 5.2 Theoretical framework 5.3 FlatMobile network: footprint segmentation 5.4 Conclusion 6. Driving among flatmobiles 6.1 Introduction 6.2 Encoder-decoder LSTM for trajectory planning 6.3 Experimental evaluation 6.4 Conclusion 7. Conclusion 7.1 Contributions 7.2 Perspectives Numéro de notice : 26769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Compiègne : 2021 Organisme de stage : Heuristique et Diagnostic des Systèmes Complexes HeuDiaSyC nature-HAL : Thèse DOI : sans Date de publication en ligne : 25/10/2021 En ligne : https://tel.hal.science/tel-03402541/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99871