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An introduction to ethics in robotics and AI / Christoph Bartneck (2021)  

Public Titre : An introduction to ethics in robotics and AI Type de document : Monographie Auteurs : Christoph Bartneck, Auteur ; Christoph Lütge, Auteur ; Alan Wagner, Auteur ; Sean Welsch, Auteur Editeur : Springer International Publishing Année de publication : 2021 Importance : 117 p Présentation : . Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-51110-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] données massives [Termes IGN] édition en libre accès [Termes IGN] éthique [Termes IGN] navigation autonome [Termes IGN] protection de la vie privée [Termes IGN] robotique [Termes IGN] utilisateur militaire [Termes IGN] véhicule sans pilote Résumé : (éditeur) This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further. Note de contenu : 1- What is AI? 2- What is ethics? 3- Trust and fairness in AI systems 4- Responsibility and liability in the case of AI systems 5- Risks in the business of AI 6- Psychological aspects of AI 7- Privacy issues of AI 8- Application areas of AI 9- Autonomous vehicles 10- Military uses of AI 11- Ethics in AI and Robotics: A Strategic Challenge Numéro de notice : 28570 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-51110-4 En ligne : https://doi.org/10.1007/978-3-030-51110-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97726

Real-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)  

Public Titre : Real-time multimodal semantic scene understanding for autonomous UGV navigation Type de document : Thèse/HDR Auteurs : Yifei Zhang, Auteur ; Fabrice Mériaudeau, Directeur de thèse ; Désiré Sidibé, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2021 Importance : 114 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse pour obtenir le doctorat de l'Université Bourgogne Franche-Comté, Spécialité Instrumentation et informatique d’image Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] données polarimétriques [Termes IGN] fusion d'images [Termes IGN] image RVB [Termes IGN] intégration de données [Termes IGN] navigation autonome [Termes IGN] segmentation sémantique [Termes IGN] temps réel [Termes IGN] véhicule sans pilote Résumé : (Auteur) Robust semantic scene understanding is challenging due to complex object types, as well as environmental changes caused by varying illumination and weather conditions. This thesis studies the problem of deep semantic segmentation with multimodal image inputs. Multimodal images captured from various sensory modalities provide complementary information for complete scene understanding. We provided effective solutions for fully-supervised multimodal image segmentation and few-shot semantic segmentation of the outdoor road scene. Regarding the former case, we proposed a multi-level fusion network to integrate RGB and polarimetric images. A central fusion framework was also introduced to adaptively learn the joint representations of modality-specific features and reduce model uncertainty via statistical post-processing.In the case of semi-supervised semantic scene understanding, we first proposed a novel few-shot segmentation method based on the prototypical network, which employs multiscale feature enhancement and the attention mechanism. Then we extended the RGB-centric algorithms to take advantage of supplementary depth cues. Comprehensive empirical evaluations on different benchmark datasets demonstrate that all the proposed algorithms achieve superior performance in terms of accuracy as well as demonstrating the effectiveness of complementary modalities for outdoor scene understanding for autonomous navigation. Note de contenu : 1. Introduction 1.1 Context and Motivation 1.2 Background and Challenges 1.3 Contributions 1.4 Organization 2. Background on Neural Networks 2.1 Basic Concepts 2.2 Neural Network Layers 2.3 Optimization 2.4 Model Training 2.5 Evaluation Metrics 2.6 Summary 3. Literature Review 3.1 Fully-supervised Semantic Image 3.2 Datasets 3.3 Summary 4. Deep Multimodal Fusion for Semantic Image Segmentation 4.1 CMNet: Deep Multimodal Fusion 4.2 A Central Multimodal Fusion Framework 4.3 Summary 5. Few-shot Semantic Image Segmentation 5.1 Introduction on Few-shot Segmentation 5.2 MAPnet: A Multiscale Attention-Based Prototypical Network 5.3 RDNet: Incorporating Depth Information into Few-shot Segmentation 5.4 Summary 6. Conclusion and Future Work 6.1 General Conclusion 6.2 Future Perspectives Numéro de notice : 26527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Instrumentation et informatique d’image : Bourgogne : 2021 nature-HAL : Thèse date de publication en ligne : 02/03/2021 En ligne : https://tel.archives-ouvertes.fr/tel-03154783/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97556

A history of laser scanning, Part 1: space and defense applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 419-429 Titre : A history of laser scanning, Part 1: space and defense applications Type de document : Article/Communication Auteurs : Adam P. Spring, Auteur Année de publication : 2020 Article en page(s) : pp 419-429 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] balayage laser [Termes IGN] capteur à balayage [Termes IGN] défense nationale [Termes IGN] histoire des sciences et techniques [Termes IGN] navigation autonome [Termes IGN] secteur spatial [Termes IGN] semis de points [Termes IGN] véhicule sans pilote Résumé : (Auteur) This article presents the origins and evolution of midrange terrestrial laser scanning (TLS), spanning primarily from the 1950s to the time of publication. Particular attention is given to developments in hardware and software that document the physical dimensions of a scene as a point cloud. These developments include parameters for accuracy, repeatability, and resolution in the midrange—millimeter and centimeter levels when recording objects at building and landscape scales up to a kilometer away. The article is split into two parts: Part one starts with early space and defense applications, and part two examines the survey applications that formed around TLS technologies in the 1990s. The origins of midrange TLS, ironically, begin in space and defense applications, which shaped the development of sensors and information processing via autonomous vehicles. Included are planetary rovers, space shuttles, robots, and land vehicles designed for relative navigation in hostile environments like space and war zones. Key people in the midrange TLS community were consulted throughout the 10-year period over which this article was written. A multilingual and multidisciplinary literature review—comprising media written or produced in Chinese, English, French, German, Japanese, Italian, and Russian—was also an integral part of this research. Numéro de notice : A2020-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.419 date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.419 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95426 [article]

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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] Intelligence artificielle [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] récepteur GPS [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] signalisation routière [Termes IGN] système de transport intelligent [Termes IGN] trajectoire [Termes IGN] véhicule sans pilote [Termes IGN] vision [Termes IGN] vision par ordinateur [Termes IGN] vision stéréoscopique [Termes IGN] vitesse 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 : 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

Deep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)  

Public Titre : Deep learning based vehicular mobility models for intelligent transportation systems Type de document : Thèse/HDR Auteurs : Jian Zhang, Auteur ; Abdelkader El Kamel, Directeur de thèse Editeur : Lille [France] : Ecole Centrale de Lille Année de publication : 2018 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée en vue d’obtenir le grade de Docteur, Spécialité : Automatique, génie informatique, traitement du signal et des images, Doctorat délivré par Centrale Lille Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] chaîne de Markov [Termes IGN] classification par réseau neuronal [Termes IGN] données de flux [Termes IGN] itération [Termes IGN] mobilité [Termes IGN] modèle de simulation [Termes IGN] sécurité routière [Termes IGN] système de transport intelligent [Termes IGN] trafic routier [Termes IGN] transport [Termes IGN] UML [Termes IGN] véhicule sans pilote Résumé : (auteur) The intelligent transportation systems gain great research interests in recent years. Although the realistic traffic simulation plays an important role, it has not received enough attention. This thesis is devoted to studying the traffic simulation in microscopic level, and proposes corresponding vehicular mobility models. Using deep learning methods, these mobility models have been proven with a promising credibility to represent the vehicles in real-world. Firstly, a data-driven neural network based mobility model is proposed. This model comes from real-world trajectory data and allows mimicking local vehicle behaviors. By analyzing the performance of this basic learning based mobility model, we indicate that an improvement is possible and we propose its specification. An HMM is then introduced. The preparation of this integration is necessary, which includes an examination of traditional dynamics based mobility models and the adaptation method of “classical” models to our situation. At last, the enhanced model is presented, and a sophisticated scenario simulation is built with it to validate the theoretical results. The performance of our mobility model is promising and implementation issues have also been discussed. Note de contenu : 1- Introduction 2- Neural network based data-driven mobility model 3- Enhanced Mobility Model with HMM 4- Experiment platform and scenario simulation Conclusions and Perspectives Numéro de notice : 25873 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique, génie informatique, traitement du signal et des images : École Centrale Lille : 2018 Organisme de stage : CRIStAL (laboratoire) nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02136219/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95562

Generating a hazard map of dynamic objects using lidar mobile mapping / Alexander Schlichting in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)  

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Quelle carte numérique pour le véhicule autonome ? / Pascal Vasseur in Transport environnement circulation TEC, n° 231 (novembre 2016)  

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Introduction to a portable stereo mapping system for unmanned vehicles / Julien Li-Chee-Ming in Geomatica, vol 66 n° 4 (December 2012)

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L'acquisition de données mobiles en pratique / Anonyme in Géomatique expert, n° 80 (01/05/2011)

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Mobile mapping and autonomous vehicle navigation / Charles K. Toth in Revue Française de Photogrammétrie et de Télédétection, n° 185 (Mars 2007)

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