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Spatial-temporal attentive LSTM for vehicle-trajectory prediction / Rui Jiang in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 354 Titre : Spatial-temporal attentive LSTM for vehicle-trajectory prediction Type de document : Article/Communication Auteurs : Rui Jiang, Auteur ; Hongyun Xu, Auteur ; Gelian Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] attention (apprentissage automatique) [Termes IGN] données spatiotemporelles [Termes IGN] navigation autonome [Termes IGN] relation spatiale [Termes IGN] système de transport intelligent [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] vision par ordinateur Résumé : (auteur) Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task because existing studies have mainly focused on the spatial interactions of adjacent vehicles regardless of the temporal dependencies. In this paper, we propose a spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) to predict vehicle trajectories. Specifically, the spatial attention mechanism is used to capture the spatial relationships among neighboring vehicles and then obtain the global spatial feature. Meanwhile, the temporal attention mechanism is designed to distinguish the effects of different historical time steps on future trajectory prediction. In addition, the motion feature of vehicles is extracted to reveal the influence of dynamic information on vehicle-trajectory prediction, and is combined with the local and global spatial features to represent the integrated features of the target vehicle at each historical moment. The experiments were conducted on public highway trajectory datasets—US-101 and I-80 in NGSIM—and the results demonstrate that our model achieves state-of-the-art prediction performance. Numéro de notice : A2022-549 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070354 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.3390/ijgi11070354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101150 [article]

Raw GIS to 3D road modeling for real-time traffic simulation / Yacine Amara in The Visual Computer, vol 38 n° 1 (January 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 1 (January 2022) . - pp 239 - 256 Titre : Raw GIS to 3D road modeling for real-time traffic simulation Type de document : Article/Communication Auteurs : Yacine Amara, Auteur ; Abdenour Amamra, Auteur ; Salim Khemis, Auteur Année de publication : 2022 Article en page(s) : pp 239 - 256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] comportement [Termes IGN] graphe topologique [Termes IGN] intersection spatiale [Termes IGN] modèle de simulation [Termes IGN] modélisation 3D [Termes IGN] navigation virtuelle [Termes IGN] planification urbaine [Termes IGN] système d'information géographique [Termes IGN] système multi-agents [Termes IGN] temps réel [Termes IGN] trafic routier [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) In this work, we propose a new approach to road modeling and 3D traffic simulation. Based on the raw geographic information system (GIS) data laid out as sparse polylines with attributes, we compute a more adequate functional description for real-time simulation of on-road vehicle animation. The proposed approach begins with a filtering/subdivision module where the raw polylines are transformed into a graph of functional road segments as arcs and the nodes as intersections. Then, the vehicle speed profile is computed based on its dynamics, its neighborhood and the curvature profile of the road. Afterward, a multi-agent system is proposed in order to handle a large number of simulated vehicle/driver couples. Finally, we deploy a 3D rendering engine to display the computed 3D simulation on screen. The resulting model satisfies most of the real road features for traffic simulation including road interchanges, roundabouts, intersections, lanes, etc. More importantly, the simulated driving qualitatively mimics the real behavior of the drivers/vehicles on the road as can be seen in the accompanying video (RTSP video). We also validate our findings with a technical assessment based on macroscopic and microscopic traffic simulation metrics in several road traffic scenarios. Numéro de notice : A2022-160 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s00371-020-02013-1 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.1007/s00371-020-02013-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99777 [article]

Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)  

Public Titre : Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions Titre original : Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions Type de document : Thèse/HDR Auteurs : Joseph Gesnouin, Auteur ; Fabien Moutarde, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2022 Importance : 171 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, Spécialité Informatique temps réel, robotique et automatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] estimation de pose [Termes IGN] image RVB [Termes IGN] instrument embarqué [Termes IGN] navigation autonome [Termes IGN] piéton [Termes IGN] reconnaissance de gestes [Termes IGN] réseau neuronal de graphes [Termes IGN] squelettisation [Termes IGN] trajectoire (véhicule non spatial) [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) The autonomous vehicle (AV) is a major challenge for the mobility of tomorrow. Progress is being made every day to achieve it; however, many problems remain to be solved to achieve a safe outcome for the most vulnerable road users (VRUs). One of the major challenge faced by AVs is the ability to efficiently drive in urban environments. Such a task requires interactions between autonomous vehicles and VRUs to resolve traffic ambiguities. In order to interact with VRUs, AVs must be able to understand their intentions and predict their incoming actions. In this dissertation, our work revolves around machine learning technology as a way to understand and predict human behaviour from visual signals and more specifically pose kinematics. Our goal is to propose an assistance system to the AV that is lightweight, scene-agnostic that could be easily implemented in any embedded devices with real-time constraints. Firstly, in the gesture and action recognition domain, we study and introduce different representations for pose kinematics, based on deep learning models as a way to efficiently leverage their spatial and temporal components while staying in an euclidean grid-space. Secondly, in the autonomous driving domain, we show that it is possible to link the posture, the walking attitude and the future behaviours of the protagonists of a scene without using the contextual information of the scene (zebra crossing, traffic light...). This allowed us to divide by a factor of 20 the inference speed of existing approaches for pedestrian intention prediction while keeping the same prediction robustness. Finally, we assess the generalization capabilities of pedestrian crossing predictors and show that the classical train-test sets evaluation for pedestrian crossing prediction, i.e., models being trained and tested on the same dataset, is not sufficient to efficiently compare nor conclude anything about their applicability in a real-world scenario. To make the research field more sustainable and representative of the real advances to come. We propose new protocols and metrics based on uncertainty estimates under domain-shift in order to reach the end-goal of pedestrian crossing behavior predictors: vehicle implementation. Note de contenu : 1- Introduction 2- Human activity recognition with pose-driven deep learning models 3- From action recognition to pedestrian discrete intention prediction 4- Assessing the generalization of pedestrian crossing predictors 5- Conclusion Numéro de notice : 24066 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique temps réel, robotique et automatique : Paris Sciences et Lettres : 2022 DOI : sans En ligne : https://pastel.archives-ouvertes.fr/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091

Mathematically optimized trajectory for terrestrial close-range photogrammetric 3D reconstruction of forest stands / Karel Kuželka in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 259 - 281 Titre : Mathematically optimized trajectory for terrestrial close-range photogrammetric 3D reconstruction of forest stands Type de document : Article/Communication Auteurs : Karel Kuželka, Auteur ; Peter Surový, Auteur Année de publication : 2021 Article en page(s) : pp 259 - 281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] détection automatique [Termes IGN] détection d'arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] optimisation (mathématiques) [Termes IGN] peuplement forestier [Termes IGN] problème du voyageur de commerce [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] séquence d'images [Termes IGN] structure-from-motion [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) Terrestrial close-range photogrammetry offers a low-cost method of three-dimensional (3D) reconstruction of forest stands that provides automatically processable 3D data that can be used to evaluate inventory parameters of forest stands and individual trees. However, fundamental methodological problems in image acquisition and processing remain. This study enhances the methodology of photogrammetric Structure from Motion reconstruction of forest stands by determining the best photographer's trajectory for image acquisition. The study comprises 1) mathematical optimization of the route in a square grid using integer programming, 2) evaluation of point clouds derived from sequences of real photographs, simulating different trajectories, and 3) verification on real trajectories. In a forest research plot, we established a 1 m square grid of 625 (i.e., 25 × 25) photographic positions, and at each position, we captured 16 photographs in uniformly spaced directions. We adopted real tree positions and diameters, and the coordinates of the photographic positions, including orientation angles of captured images, were recorded. We then formulated an integer programming optimization model to find the most efficient trajectory that provided coverage of all sides of all trees with sufficient counts of images. Subsequently, we used the 10,000 captured images to produce image subsets simulating image sequences acquired during the photographer's movement along 84 different systematic trajectories of seven patterns based on either parallel lines or concentric orbits. 3D point clouds derived from the simulated image sequences were evaluated for their suitability for automatic tree detection and estimation of diameters at breast height. The results of the integer programming model indicated that the optimal trajectory consisted of parallel line segments if the camera is pointed forward – in the travel direction, or concentric orbits if the camera is pointed to a side – perpendicular to the travel direction. With point clouds derived from the images of the simulated trajectories, the best diameter estimates on automatically detected trees were achieved with trajectories consisting of parallel lines in two perpendicular directions where each line was passed in both opposite directions. For efficient image acquisition, resulting in point clouds of reasonable quality with low counts of images, a trajectory consisting of concentric orbits, including the plot perimeter with the camera pointed towards the plot center, proved to be the best. Results of simulated trajectories were verified with the photogrammetric reconstruction of the forest stand based on real trajectories for six patterns. The mathematical optimization was consistent with the results of the experiment, which indicated that mathematical optimization may represent a valid tool for planning trajectories for photogrammetric 3D reconstruction of scenes in general. Numéro de notice : A2021-562 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.013 Date de publication en ligne : 02/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98122 [article]

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Geographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica [en ligne], vol 25 n° 3 (July 2021)  

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 3 (July 2021) . - pp 485 - 512 Titre : Geographical and temporal huff model calibration using taxi trajectory data Type de document : Article/Communication Auteurs : Shuhui Gong, Auteur ; John Cartlidge, Auteur ; Ruibin Bai, Auteur ; Yang Yue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 485 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attractivité (aménagement) [Termes IGN] étalonnage de modèle [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] régression des moindres carrés partiels [Termes IGN] régression géographiquement pondérée [Termes IGN] Shenzhen [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre’s attractiveness and the cost of a customer’s travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales’ and rental prices in Shenzhen and New York as a proxy for customers’ wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales’ and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers’ spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre’s attractiveness. We also discover customers’ sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents’ travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents’ length of spare time. Numéro de notice : A2021-973 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10707-019-00390-x Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s10707-019-00390-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100392 [article]

Mapping trajectories and flows: facilitating a human-centered approach to movement data analytics / Somayeh Dodge in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

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A scalable method to construct compact road networks from GPS trajectories / Yuejun Guo in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

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Trajectory and image-based detection and identification of UAV / Yicheng Liu in The Visual Computer, vol 37 n° 7 (July 2021)  

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A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks / Bozhao Li in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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Enhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Learning from GPS trajectories of floating car for CNN-based urban road extraction with high-resolution satellite imagery / Ju Zhang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

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Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Network-constrained bivariate clustering method for detecting urban black holes and volcanoes / Qiliang Liu in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

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Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations / Guanxu Chen in Journal of geodesy, vol 94 n° 6 (June 2020)  

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