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Auteur Corentin Sanchez |
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Titre : A world model enabling information integrity for autonomous vehicles Type de document : Thèse/HDR Auteurs : Corentin Sanchez, Auteur ; Philippe Bonnifait, Directeur de thèse ; Philippe Xu, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2022 Importance : 198 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat de l'Université de Technologie de Compiègne, Spécialité Automatique et RobotiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] carte routière
[Termes IGN] données multisources
[Termes IGN] information sémantique
[Termes IGN] intégrité des données
[Termes IGN] milieu urbain
[Termes IGN] navigation autonome
[Termes IGN] raisonnement
[Termes IGN] réseau routier
[Termes IGN] robot mobile
[Termes IGN] sécurité routière
[Termes IGN] véhicule sans pilote
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) To drive in complex urban environments, autonomous vehicles need to understand their driving context. This task, also known as the situation awareness, relies on an internal virtual representation of the world made by the vehicle, called world model. This representation is generally built from information provided by multiple sources. High definition navigation maps supply prior information such as road network topology, geometric description of the carriageway, and semantic information including traffic laws. The perception system provides a description of the space and of road users evolving in the vehicle surroundings. Conjointly, they provide representations of the environment (static and dynamic) and allow to model interactions. In complex situations, a reliable and non-misleading world model is mandatory to avoid inappropriate decision-making and to ensure safety. The goal of this PhD thesis is to propose a novel formalism on the concept of world model that fulfills the situation awareness requirements for an autonomous vehicle. This world model integrates prior knowledge on the road network topology, a lane-level grid representation, its prediction over time and more importantly a mechanism to control and monitor the integrity of information. The concept of world model is present in many autonomous vehicle architectures but may take many various forms and sometimes only implicitly. In some work, it is part of the perception process when in some other it is part of a decisionmaking process. The first contribution of this thesis is a survey on the concept of world model for autonomous driving covering different levels of abstraction for information representation and reasoning. Then, a novel representation is proposed for the world model at the tactical level combining dynamic objects and spatial occupancy information. First, a graph based top-down approach using a high-definition map is proposed to extract the areas of interests with respect to the situation from the vehicle's perspective. It is then used to build a Lane Grid Map (LGM), which is an intermediate space state representation from the ego-vehicle point of view. A top-down approach is chosen to assess and characterize the relevant information of the situation. Additionally to classical free-occupied states, the unknown state is further characterized by the notions of neutralized and safe areas that provide a deeper level of understanding of the situation. Another contribution to the world model is an integrity management mechanism that is built upon the LGM representation. It consists in managing the spatial sampling of the grid cells in order to take into account localization and perception errors and to avoid misleading information. Regardless of the confidence on localization and perception information, the LGM is capable of providing reliable information to decision making in order not to take hazardous decisions.The last part of the situation awareness strategy is the prediction of the world model based on the LGM representation. The main contribution is to show how a classical object-level prediction fits this representation and that the integrity can also be extended at the prediction stage. It is also depicted how a neutralized area can be used in the prediction stage to provide a better situation prediction. The work relies on experimental data in order to demonstrate a real application of a complex situation awareness representation. The approach is evaluated with real data obtained thanks to several experimental vehicles equipped with LiDAR sensors and IMU with RTK corrections in the city of Compi_egne. A high-definition map has also been used in the framework of the SIVALab joint laboratory between Renault and Heudiasyc CNRS-UTC. The world model module has been implemented (with ROS software) in order to fulfll real-time application and is functional on the experimental vehicles for live demonstrations. Note de contenu : General introduction
1- World model for autonomous vehicules
2- An architecture for WM
3- A lane level world model
4- Set-based LGM prediction
General conclusionNuméro de notice : 24089 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique et Robotique : UTC Compiègne : 2022 Organisme de stage : Laboratoire Heudiasyc DOI : sans En ligne : https://www.theses.fr/2022COMP2683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102509