Relevant bibliographies by topics / Evidential occupancy grids

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Evidential occupancy grids'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Evidential occupancy grids"

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Yu, Chunlei, Veronique Cherfaoui, Philippe Bonnifait, and Dian-ge Yang. "Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids." Sensors 20, no. 2 (2020): 352. http://dx.doi.org/10.3390/s20020352.

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Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for s
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Jo, Kichun, Sungjin Cho, Chansoo Kim, et al. "Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping." Sensors 18, no. 12 (2018): 4119. http://dx.doi.org/10.3390/s18124119.

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Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update
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Richter, Sven, Yiqun Wang, Johannes Beck, Sascha Wirges, and Christoph Stiller. "Semantic Evidential Grid Mapping Using Monocular and Stereo Cameras." Sensors 21, no. 10 (2021): 3380. http://dx.doi.org/10.3390/s21103380.

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Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In or
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Godaliyadda, G. M. Dilshan P., Vijay Pothukuchi, and JuneChul Roh. "Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping." Electronic Imaging 2020, no. 16 (2020): 255–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-203.

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Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LI
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Dissertations / Theses on the topic "Evidential occupancy grids"

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Yu, Chunlei. "Contribution to evidential models for perception grids : application to intelligent vehicle navigation." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2293.

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Pour les véhicules intelligents, un système de perception est un élément clé pour caractériser en temps réel un modèle de l’environnement de conduite autour du véhicule. Lors de la modélisation de l’environnement, les informations relatives aux obstacles doivent être gérées prioritairement car les collisions peuvent être mortelles pour les autres usagers de la route ou pour les passagers à bord du véhicule considéré. La caractérisation de l’espace occupé est donc cruciale mais pas suffisante pour les véhicules autonomes puisque le système de contrôle a besoin de trouver l’espace navigable pour
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Thornton, Douglas Anthony. "High Fidelity Localization and Map Building from an Instrumented Probe Vehicle." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483637485442285.

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Kurdej, Marek. "Exploitation of map data for the perception of intelligent vehicles." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2174/document.

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La plupart des logiciels contrôlant les véhicules intelligents traite de la compréhension de la scène. De nombreuses méthodes existent actuellement pour percevoir les obstacles de façon automatique. La majorité d’entre elles emploie ainsi les capteurs extéroceptifs comme des caméras ou des lidars. Cette thèse porte sur les domaines de la robotique et de la fusion d’information et s’intéresse aux systèmes d’information géographique. Nous étudions ainsi l’utilité d’ajouter des cartes numériques, qui cartographient le milieu urbain dans lequel évolue le véhicule, en tant que capteur virtuel améli
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Capellier, Édouard. "Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2534.

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L’apprentissage machine a révolutionné la manière dont les problèmes de perception sont, actuellement, traités. En effet, la plupart des approches à l’état de l’art, dans de nombreux domaines de la vision par ordinateur, se reposent sur des réseaux de neurones profonds. Au moment de déployer, d’évaluer, et de fusionner de telles approches au sein de véhicules autonomes, la question de la représentation des connaissances extraites par ces approches se pose. Dans le cadre de ces travaux de thèse, effectués au sein de Renault SAS, nous avons supposé qu’une représentation crédibiliste permettait d
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Conference papers on the topic "Evidential occupancy grids"

1

Camarda, Federico, Franck Davoine, and Veronique Cherfaoui. "Fusion of evidential occupancy grids for cooperative perception." In 2018 13th Annual Conference on System of Systems Engineering (SoSE). IEEE, 2018. http://dx.doi.org/10.1109/sysose.2018.8428723.

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Duan, Jianmin, Lu Ren, Longjie Li, and Dan Liu. "Moving Objects Detection in Evidential Occupancy Grids Using Laser Radar." In 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2016. http://dx.doi.org/10.1109/ihmsc.2016.211.

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Steyer, Sascha, Georg Tanzmeister, and Dirk Wollherr. "Object tracking based on evidential dynamic occupancy grids in urban environments." In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017. http://dx.doi.org/10.1109/ivs.2017.7995855.

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Yu, Chunlei, Veronique Cherfaoui, and Philippe Bonnifait. "Evidential occupancy grid mapping with stereo-vision." In 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225768.

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Wirges, Sascha, Christoph Stiller, and Felix Hartenbach. "Evidential Occupancy Grid Map Augmentation using Deep Learning." In 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018. http://dx.doi.org/10.1109/ivs.2018.8500635.

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Mouhagir, Hafida, Veronique Cherfaoui, Reine Talj, Francois Aioun, and Franck Guillemard. "Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017. http://dx.doi.org/10.1109/itsc.2017.8317808.

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