Academic literature on the topic 'Intelligent vehicles localization'
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Journal articles on the topic "Intelligent vehicles localization"
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Hui Fang, Chunxiang Wang, Ming Yang, and Ruqing Yang. "Ground-Texture-Based Localization for Intelligent Vehicles." IEEE Transactions on Intelligent Transportation Systems 10, no. 3 (September 2009): 463–68. http://dx.doi.org/10.1109/tits.2009.2026445.
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Wan, Liangtian, Mingyue Zhang, Lu Sun, and Xianpeng Wang. "Machine Learning Empowered IoT for Intelligent Vehicle Location in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (August 31, 2021): 1–25. http://dx.doi.org/10.1145/3448612.
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Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.
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Wang, Mingyang, Xinbo Chen, Pengyuan Lv, Baobao Jin, Wei Wang, and Yong Shen. "UWB Based Relative Planar Localization with Enhanced Precision for Intelligent Vehicles." Actuators 10, no. 7 (June 26, 2021): 144. http://dx.doi.org/10.3390/act10070144.
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Along with the rapid development of advanced driving assistance systems for intelligent vehicles, essential functions such as forward collision warning and collaborative cruise control need to detect the relative positions of surrounding vehicles. This paper proposes a relative planar localization system based on the ultra-wideband (UWB) ranging technology. Three UWB modules are installed on the top of each vehicle. Because of the limited space on the vehicle roof compared with the ranging error, the traditional triangulation method leads to significant positioning errors. Therefore, an optimal localization algorithm combining homotopy and the Levenberg–Marquardt method is first proposed to enhance the precision. The triangular side lengths and directed area are introduced as constraints. Secondly, a UWB sensor error self-correction method is presented to further improve the ranging accuracy. Finally, we carry out simulations and experiments to show that the presented algorithm in this paper significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.
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Huang, Gang, Zhaozheng Hu, Mengchao Mu, Xianglong Wang, and Fan Zhang. "Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 19, 2019): 791–800. http://dx.doi.org/10.1177/0361198119857032.
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Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.
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Yang, Haixu, Jichao Hong, Lingjun Wei, Xun Gong, and Xiaoming Xu. "Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication." Electronics 11, no. 19 (October 9, 2022): 3247. http://dx.doi.org/10.3390/electronics11193247.
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Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios.
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Li, Zheng Feng, and Lian Zhou Gao. "Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System." Applied Mechanics and Materials 548-549 (April 2014): 1407–14. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1407.
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This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application
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Gao, Lian Zhou. "Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System." Applied Mechanics and Materials 539 (July 2014): 867–73. http://dx.doi.org/10.4028/www.scientific.net/amm.539.867.
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This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application
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Ma, Fangwu, Jinzhu Shi, Liang Wu, Kai Dai, and Shouren Zhong. "Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization." Sensors 20, no. 20 (October 10, 2020): 5757. http://dx.doi.org/10.3390/s20205757.
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The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.
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Nakrani, Naitik, and Maulin M. Joshi. "An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 687. http://dx.doi.org/10.11591/ijai.v10.i3.pp687-697.
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In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.
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Chen, Xiaobo, Jianyu Ji, and Yanjun Wang. "Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication." Sensors 20, no. 11 (June 5, 2020): 3212. http://dx.doi.org/10.3390/s20113212.
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The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.
Dissertations / Theses on the topic "Intelligent vehicles localization"
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Lu, Wenjie. "Contributions to Lane Marking Based Localization for Intelligent Vehicles." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112017/document.
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Les applications pour véhicules autonomes et les systèmes d’aide avancée à la conduite (Advanced Driving Assistance Systems - ADAS) mettent en oeuvre des processus permettant à des systèmes haut niveau de réaliser une prise de décision. Pour de tels systèmes, la connaissance du positionnement précis (ou localisation) du véhicule dans son environnement est un pré-requis nécessaire. Cette thèse s’intéresse à la détection de la structure de scène, au processus de localisation ainsi qu’à la modélisation d’erreurs. A partir d’un large spectre fonctionnel de systèmes de vision, de l’accessibilité d’un système de cartographie ouvert (Open Geographical Information Systems - GIS) et de la large diffusion des systèmes de positionnement dans les véhicules (Global Positioning System - GPS), cette thèse étudie la performance et la fiabilité d’une méthode de localisation utilisant ces différentes sources. La détection de marquage sur la route réalisée par caméra monoculaire est le point de départ permettant de connaître la structure de la scène. En utilisant, une détection multi-noyau avec pondération hiérarchique, la méthode paramétrique proposée effectue la détection et le suivi des marquages sur la voie du véhicule en temps réel. La confiance en cette source d’information a été quantifiée par un indicateur de vraisemblance. Nous proposons ensuite un système de localisation qui fusionne des informations de positionnement (GPS), la carte (GIS) et les marquages détectés précédemment dans un cadre probabiliste basé sur un filtre particulaire. Pour ce faire, nous proposons d’utiliser les marquages détectés non seulement dans l’étape de mise en correspondance des cartes mais aussi dans la modélisation de la trajectoire attendue du véhicule. La fiabilité du système de localisation, en présence d’erreurs inhabituelles dans les différentes sources d’information, est améliorée par la prise en compte de différents indicateurs de confiance. Ce mécanisme est par la suite utilisé pour identifier les sources d’erreur. Cette thèse se conclut par une validation expérimentale des méthodes proposées dans des situations réelles de conduite. Leurs performances ont été quantifiées en utilisant un véhicule expérimental et des données en libre accès sur internetAutonomous Vehicles (AV) applications and Advanced Driving Assistance Systems (ADAS) relay in scene understanding processes allowing high level systems to carry out decision marking. For such systems, the localization of a vehicle evolving in a structured dynamic environment constitutes a complex problem of crucial importance. Our research addresses scene structure detection, localization and error modeling. Taking into account the large functional spectrum of vision systems, the accessibility of Open Geographical Information Systems (GIS) and the widely presence of Global Positioning Systems (GPS) onboard vehicles, we study the performance and the reliability of a vehicle localization method combining such information sources. Monocular vision–based lane marking detection provides key information about the scene structure. Using an enhanced multi-kernel framework with hierarchical weights, the proposed parametric method performs, in real time, the detection and tracking of the ego-lane marking. A self-assessment indicator quantifies the confidence of this information source. We conduct our investigations in a localization system which tightly couples GPS, GIS and lane makings in the probabilistic framework of Particle Filter (PF). To this end, it is proposed the use of lane markings not only during the map-matching process but also to model the expected ego-vehicle motion. The reliability of the localization system, in presence of unusual errors from the different information sources, is enhanced by taking into account different confidence indicators. Such a mechanism is later employed to identify error sources. This research concludes with an experimental validation in real driving situations of the proposed methods. They were tested and its performance was quantified using an experimental vehicle and publicly available datasets
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Welte, Anthony. "Spatio-temporal data fusion for intelligent vehicle localization." Thesis, Compiègne, 2020. http://bibliotheque.utc.fr/EXPLOITATION/doc/IFD/2020COMP2572.
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La localisation précise constitue une brique essentielle permettant aux véhicules de naviguer de manière autonome sur la route. Cela peut être atteint à travers les capteurs déjà existants, de nouvelles technologies (Iidars, caméras intelligentes) et des cartes haute définition. Dans ce travail, l'intérêt d'enregistrer et réutiliser des informations sauvegardées en mémoire est exploré. Les systèmes de localisation doivent permettre une estimation à haute fréquence, des associations de données, de la calibration et de la détection d'erreurs. Une architecture composée de plusieurs couches de traitement est proposée et étudiée. Une couche principale de filtrage estime la pose tandis que les autres couches abordent les problèmes plus complexes. L'estimation d'état haute fréquence repose sur des mesures proprioceptives. La calibration du système est essentielle afin d'obtenir une pose précise. En gardant les états estimés et les observations en mémoire, les modèles d'observation des capteurs peuvent être calibrés à partir des estimations lissées. Les Iidars et les caméras intelligentes fournissent des mesures qui peuvent être utilisées pour la localisation mais soulèvent des problèmes d'association de données. Dans cette thèse, le problème est abordé à travers une fenêtre spatio-temporelle, amenant une image plus détaillée de l'environnement. Le buffer d'états est ajusté avec les observations et toutes les associations possibles. Bien que l'utilisation d'amers cartographiés permette d'améliorer la localisation, cela n'est possible que si la carte est fiable. Une approche utilisant les résidus lissés a posteriori a été développée pour détecter ces changements de carteLocalization is an essential basic capability for vehicles to be able to navigate autonomously on the road. This can be achieved through already available sensors and new technologies (Iidars, smart cameras). These sensors combined with highly accurate maps result in greater accuracy. In this work, the benefits of storing and reusing information in memory (in data buffers) are explored. Localization systems need to perform a high-frequency estimation, map matching, calibration and error detection. A framework composed of several processing layers is proposed and studied. A main filtering layer estimates the vehicle pose while other layers address the more complex problems. High-frequency state estimation relies on proprioceptive measurements combined with GNSS observations. Calibration is essential to obtain an accurate pose. By keeping state estimates and observations in a buffer, the observation models of these sensors can be calibrated. This is achieved using smoothed estimates in place of a ground truth. Lidars and smart cameras provide measurements that can be used for localization but raise matching issues with map features. In this work, the matching problem is addressed on a spatio-temporal window, resulting in a more detailed pictur of the environment. The state buffer is adjusted using the observations and all possible matches. Although using mapped features for localization enables to reach greater accuracy, this is only true if the map can be trusted. An approach using the post smoothing residuals has been developed to detect changes and either mitigate or reject the affected features
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Rodríguez, Florez Sergio Alberto. "Contributions by vision systems to multi-sensor object localization and tracking for intelligent vehicles." Compiègne, 2010. http://www.theses.fr/2010COMP1910.
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Les systèmes d’aide à la conduite peuvent améliorer la sécurité routière en aidant les utilisateurs via des avertissements de situations dangereuses ou en déclenchant des actions appropriées en cas de collision imminente (airbags, freinage d’urgence, etc). Dans ce cas, la connaissance de la position et de la vitesse des objets mobiles alentours constitue une information clé. C’est pourquoi, dans ce travail, nous nous focalisons sur la détection et le suivi d’objets dans une scène dynamique. En remarquant que les systèmes multi-caméras sont de plus en plus présents dans les véhicules et en sachant que le lidar est performant pour la détection d’obstacles, nous nous intéressons à l’apport de la vision stéréoscopique dans la perception géométrique multimodale de l’environnement. Afin de fusionner les informations géométriques entre le lidar et le système de vision, nous avons développé un procédé de calibrage qui détermine les paramètres extrinsèques et évalue les incertitudes sur ces estimations. Nous proposons ensuite une méthode d’odométrie visuelle temps-réel permettant d’estimer le mouvement propre du véhicule afin de simplifier l’analyse du mouvement des objets dynamiques. Dans un second temps, nous montrons comment l’intégrité de la détection et du suivi des objets par lidar peut être améliorée en utilisant une méthode de confirmation visuelle qui procède par reconstruction dense de l’environnement 3D. Pour finir, le système de perception multimodal a été intégré sur une plateforme automobile, ce qui a permis de tester expérimentalement les différentes approches proposées dans des situations routières en environnement non contrôléAdvanced Driver Assistance Systems (ADAS) can improve road safety by supporting the driver through warnings in hazardous circumstances or triggering appropriate actions when facing imminent collision situations (e. G. Airbags, emergency brake systems, etc). In this context, the knowledge of the location and the speed of the surrounding mobile objects constitute a key information. Consequently, in this work, we focus on object detection, localization and tracking in dynamic scenes. Noticing the increasing presence of embedded multi-camera systems on vehicles and recognizing the effectiveness of lidar automotive systems to detect obstacles, we investigate stereo vision systems contributions to multi-modal perception of the environment geometry. In order to fuse geometrical information between lidar and vision system, we propose a calibration process which determines the extrinsic parameters between the exteroceptive sensors and quantifies the uncertainties of this estimation. We present a real-time visual odometry method which estimates the vehicle ego-motion and simplifies dynamic object motion analysis. Then, the integrity of the lidar-based object detection and tracking is increased by the means of a visual confirmation method that exploits stereo-vision 3D dense reconstruction in focused areas. Finally, a complete full scale automotive system integrating the considered perception modalities was implemented and tested experimentally in open road situations with an experimental car
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BALLARDINI, AUGUSTO LUIS. "Matching heterogeneous sensing pipelines to digital maps for ego-vehicle localization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/148691.
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In this thesis, we present a probabilistic framework for ego-vehicle localization called Road Layout Estimation framework. The main contribution to the vehicle localization problem is the synergistic exploitation of heterogeneous sensing pipelines, as well as their matching with respect to the OpenStreetMap service. The approach is validated in different ways by exploiting different visual clues. Firstly by using the road graph provided by the OpenStreetMap service, then exploiting high-level features like intersections between roads, buildings façades, and other road features.
Regarding the effectiveness of the road-graph exploitation, its is proven by achieving real-time computation with state-of-the-art results on a set of ten not trivial runs from the KITTI dataset, including both urban/residential and highway/road scenarios. Moreover, a probabilistic approach for detecting and classifying urban road intersections from a moving vehicle is presented. The approach is based on images from an on-board stereo rig. It relies on the detection of the road ground plane on one side, and on a pixel-level classification of the observed scene on the other. The two processing pipelines are then integrated and the parameters of the road components, i.e., the intersection geometry, are inferred. As opposed to other state-of-the-art off-line methods, which require processing of the whole video sequence up to when the vehicle is inside the intersection, our approach integrates the image data by means of an on-line procedure. The experiments have been performed on the well-known KITTI datasets as well, allowing the community to perform future comparisons. Besides the pure road interpretation schemes, in this work we also present a technique that takes advantage of detected building façades and OpenStreetMaps building data to improve the localization of an autonomous vehicle driving in an urban scenario. The proposed approach also leverages images from the stereo rig mounted on the vehicle to produce a mathematical representation of the buildings' façades within the field of view. This representation is matched against the outlines of the surrounding buildings as they are available on OpenStreetMaps. All the retrieved features are fed into our probabilistic framework, in order to produce an accurate lane-level localization of the vehicle in urban contexts.
Finally, as to achieve a lane-level localization also in highway scenarios, we propose two methods that allow the framework to leverage the lane number and the road width. The proposed approaches have been tested under real traffic conditions, showing satisfactory performances with respect to the map-matching-only settings and compensating the noisy measures of a basic line detector.
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Tao, Zui. "Autonomous road vehicles localization using satellites, lane markings and vision." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2261/document.
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L'estimation de la pose (position et l'attitude) en temps réel est une fonction clé pour les véhicules autonomes routiers. Cette thèse vise à étudier des systèmes de localisation pour ces véhicules en utilisant des capteurs automobiles à faible coût. Trois types de capteurs sont considérés : des capteurs à l'estime qui existent déjà dans les automobiles modernes, des récepteurs GNSS mono-fréquence avec antenne patch et une caméra de détection de la voie regardant vers l’avant. Les cartes très précises sont également des composants clés pour la navigation des véhicules autonomes. Dans ce travail, une carte de marquage de voies avec une précision de l’ordre du décimètre est considérée. Le problème de la localisation est étudié dans un repère de travail local Est-Nord-Haut. En effet, les sorties du système de localisation sont utilisées en temps réel comme entrées dans un planificateur de trajectoire et un contrôleur de mouvement pour faire en sorte qu’un véhicule soit capable d'évoluer au volant de façon autonome à faible vitesse avec personne à bord. Ceci permet de développer des applications de voiturier autonome aussi appelées « valet de parking ». L'utilisation d'une caméra de détection de voie rend possible l’exploitation des informations de marquage de voie stockées dans une carte géoréférencée. Un module de détection de marquage détecte la voie hôte du véhicule et fournit la distance latérale entre le marquage de voie détecté et le véhicule. La caméra est également capable d'identifier le type des marquages détectés au sol (par exemple, de type continu ou pointillé). Comme la caméra donne des mesures relatives, une étape importante consiste à relier les mesures à l'état du véhicule. Un modèle d'observation raffiné de la caméra est proposé. Il exprime les mesures métriques de la caméra en fonction du vecteur d'état du véhicule et des paramètres des marquages au sol détectés. Cependant, l'utilisation seule d'une caméra a des limites. Par exemple, les marquages des voies peuvent être absents dans certaines parties de la zone de navigation et la caméra ne parvient pas toujours à détecter les marquages au sol, en particulier, dans les zones d’intersection. Un récepteur GNSS, qui est obligatoire pour le démarrage à froid, peut également être utilisé en continu dans le système de localisation multi-capteur du fait qu’il permet de compenser la dérive de l’estime. Les erreurs de positionnement GNSS ne peuvent pas être modélisées simplement comme des bruits blancs, en particulier avec des récepteurs mono-fréquence à faible coût travaillant de manière autonome, en raison des perturbations atmosphériques sur les signaux des satellites et les erreurs d’orbites. Un récepteur GNSS peut également être affecté par de fortes perturbations locales qui sont principalement dues aux multi-trajets. Cette thèse étudie des modèles formeurs de biais d’erreur GNSS qui sont utilisés dans le solveur de localisation en augmentant le vecteur d'état. Une variation brutale due à multi-trajet est considérée comme une valeur aberrante qui doit être rejetée par le filtre. Selon le flux d'informations entre le récepteur GNSS et les autres composants du système de localisation, les architectures de fusion de données sont communément appelées « couplage lâche » (positions et vitesses GNSS) ou « couplage serré » (pseudo-distance et Doppler sur les satellites en vue). Cette thèse étudie les deux approches. En particulier, une approche invariante selon la route est proposée pour gérer une modélisation raffinée de l'erreur GNSS dans l'approche par couplage lâche puisque la caméra ne peut améliorer la performance de localisation que dans la direction latérale de la routeEstimating the pose (position and attitude) in real-time is a key function for road autonomous vehicles. This thesis aims at studying vehicle localization performance using low cost automotive sensors. Three kinds of sensors are considered : dead reckoning (DR) sensors that already exist in modern vehicles, mono-frequency GNSS (Global navigation satellite system) receivers with patch antennas and a frontlooking lane detection camera. Highly accurate maps enhanced with road features are also key components for autonomous vehicle navigation. In this work, a lane marking map with decimeter-level accuracy is considered. The localization problem is studied in a local East-North-Up (ENU) working frame. Indeed, the localization outputs are used in real-time as inputs to a path planner and a motion generator to make a valet vehicle able to drive autonomously at low speed with nobody on-board the car. The use of a lane detection camera makes possible to exploit lane marking information stored in the georeferenced map. A lane marking detection module detects the vehicle’s host lane and provides the lateral distance between the detected lane marking and the vehicle. The camera is also able to identify the type of the detected lane markings (e.g., solid or dashed). Since the camera gives relative measurements, the important step is to link the measures with the vehicle’s state. A refined camera observation model is proposed. It expresses the camera metric measurements as a function of the vehicle’s state vector and the parameters of the detected lane markings. However, the use of a camera alone has some limitations. For example, lane markings can be missing in some parts of the navigation area and the camera sometimes fails to detect the lane markings in particular at cross-roads. GNSS, which is mandatory for cold start initialization, can be used also continuously in the multi-sensor localization system as done often when GNSS compensates for the DR drift. GNSS positioning errors can’t be modeled as white noises in particular with low cost mono-frequency receivers working in a standalone way, due to the unknown delays when the satellites signals cross the atmosphere and real-time satellites orbits errors. GNSS can also be affected by strong biases which are mainly due to multipath effect. This thesis studies GNSS biases shaping models that are used in the localization solver by augmenting the state vector. An abrupt bias due to multipath is seen as an outlier that has to be rejected by the filter. Depending on the information flows between the GNSS receiver and the other components of the localization system, data-fusion architectures are commonly referred to as loosely coupled (GNSS fixes and velocities) and tightly coupled (raw pseudoranges and Dopplers for the satellites in view). This thesis investigates both approaches. In particular, a road-invariant approach is proposed to handle a refined modeling of the GNSS error in the loosely coupled approach since the camera can only improve the localization performance in the lateral direction of the road. Finally, this research discusses some map-matching issues for instance when the uncertainty domain of the vehicle state becomes large if the camera is blind. It is challenging in this case to distinguish between different lanes when the camera retrieves lane marking measurements.As many outdoor experiments have been carried out with equipped vehicles, every problem addressed in this thesis is evaluated with real data. The different studied approaches that perform the data fusion of DR, GNSS, camera and lane marking map are compared and several conclusions are drawn on the fusion architecture choice
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Li, Franck. "Lane-level vehicle localization with integrity monitoring for data aggregation." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2458/document.
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Les informations contenues dans les cartes routières numériques revêtent une importance grandissante dans le domaine des véhicules intelligents. La prise en compte d’environnements de plus en plus complexes a augmenté le niveau de précision exigé des informations cartographiques. Les cartes routières numériques, considérées ici comme des bases de données géographiques, contiennent des informations contextuelles sur le réseau routier, facilitant la compréhension correcte de l’environnement. En les combinant avec les données provenant des capteurs embarqués, une représentation plus fine de l’environnement peut être obtenue, améliorant grandement la compréhension de contexte du véhicule et la prise de décision. La performance des différents capteurs peut varier grandement en fonction du lieu considéré, ceci étant principalement dû à des facteurs environnementaux. Au contraire, une carte peut fournir ses informations de manière fiable, sans être affectée par ces éléments extérieurs, mais pour cela, elle doit reposer sur un autre élément essentiel : une source de localisation. Le secteur automobile utilise les systèmes de localisation globale par satellite (GNSS) à des fins de localisation absolue, mais cette solution n’est pas parfaite, étant soumise à différentes sources d’erreur. Ces erreurs sont elles aussi dépendantes de l’environnent d’évolution du véhicule (par exemple, des multi-trajets causés par des bâtiments). Nous sommes donc en présence de deux systèmes centraux, dont les performances sont d´dépendantes du lieu considéré. Cette étude se focalise sur leur dénominateur commun : la carte routière numérique, et son utilisation en tant qu’outil d’évaluation de leur performance. L’idée développée durant cette thèse est d’utiliser la carte en tant que canevas d’apprentissage, pour stocker des informations géoréférencées sur la performance des diésèrent capteurs équipant le véhicule, au cours de trajets répétitifs. Pour cela, une localisation robuste, relative à la carte, est nécessaire au travers d’une méthode de map-matching. La problématique principale réside dans la différence de précision entre la carte et le positionnement GNSS, créant des situations ambigües. Durant cette thèse, un algorithme de map-matching a été conçu pour gérer ces ambigüités en fournissant des hypothèses multiples lorsque nécessaire. L’objectif est d’assurer l’intégrité de l’algorithme en retournant un ensemble d’hypothèses contenant l’hypothèse correcte avec une grande probabilité. Cet algorithme utilise les capteurs proprioceptifs dans une approche de navigation à l’estime aidée d’informations cartographiques. Une procédure d’évaluation de cohérence, utilisant le GNSS comme information redondante de positionnement est ensuite appliquée, visant à isoler une hypothèse cohérente unique qui pourra ainsi être utilisée avec confiance dans le processus d’écriture dans la carte. L’utilisation de la carte numérique en écriture/lecture a été évaluée et la procédure complète d’écriture a été testée sur des données réelles, enregistrées par des véhicules expérimentaux sur route ouverteThe information stored in digital road maps has become very important for intelligent vehicles. As intelligent vehicles address more complex environments, the accuracy requirements for this information have increased. Regarded as a geographic database, digital road maps contain contextual information about the road network, crucial for a good understanding of the environment. When combined with data acquired from on-board sensors, a better representation of the environment can be made, improving the vehicle’s situation understanding. Sensors performance can vary drastically depending on the location of the vehicle, mainly due to environmental factors. Comparatively, a map can provide prior information more reliably but to do so, it depends on another essential component: a localization system. Global Navigation Satellite Systems (GNSS) are commonly used in automotive to provide an absolute positioning of the vehicle, but its accuracy is not perfect: GNSS are prone to errors, also depending greatly on the environment (e.g., multipaths). Perception and localization systems are two important components of an intelligent vehicle whose performances vary in function of the vehicle location. This research focuses on their common denominator, the digital road map, and its use as a tool to assess their performance. The idea developed during this thesis is to use the map as a learning canvas, to store georeferenced information about the performance of the sensors during repetitive travels. This requires a robust localization with respect to the map to be available, through a process of map-matching. The main problematic is the discrepancy between the accuracy of the map and of the GNSS, creating ambiguous situations. This thesis develops a map-matching algorithm designed to cope with these ambiguities by providing multiple hypotheses when necessary. The objective is to ensure the integrity of the result by returning a hypothesis set containing the correct matching with high probability. The method relies on proprioceptive sensors via a dead-reckoning approach aided by the map. A coherence checking procedure using GNSS redundant information is then applied to isolate a single map-matching result that can be used to write learning data with confidence in the map. The possibility to handle the digital map in read/write operation has been assessed and the whole writing procedure has been tested on data recorded by test vehicles on open roads
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Balakrishnan, Arjun. "Integrity Analysis of Data Sources in Multimodal Localization System." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG060.
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Les véhicules intelligents sont un élément clé pour des systèmes de transport plus sûrs, efficaces et accessibles à travers le monde. En raison de la multitude de sources de données et de processus associés aux véhicules intelligents, la fiabilité de l'ensemble du système dépend fortement de la possibilité d'erreurs ou de mauvaises performances observées dans ses composants. Dans notre travail, nous nous intéressons à la tâche critique de localisation des véhicules intelligents et relevons les défis de la surveillance de l'intégrité des sources de données utilisées dans la localisation. La contribution clé de notre recherche est la proposition d'un nouveau protocole d'intégrité en combinant les concepts d'intégrité des systèmes d'information et les concepts d'intégrité existants dans les Systèmes de Transport Intelligents (STI). Un cadre de surveillance de l'intégrité basé sur le protocole d'intégrité proposé qui peut gérer les problèmes de localisation multimodale est développé. Dans la première étape, une preuve de concept pour ce cadre est développée sur la base d'une estimation de cohérence croisée des sources de données à l'aide de modèles polynomiaux. Sur la base des observations de la première étape, une représentation des données «Feature Grid» est proposée dans la deuxième étape et un prototype généralisé pour le cadre est mis en œuvre. Le cadre est testé sur les autoroutes ainsi que dans des scénarios urbains complexes pour démontrer que le cadre proposé est capable de fournir des estimations d'intégrité continue des sources de données multimodales utilisées dans la localisation intelligente des véhiculesIntelligent vehicles are a key component in humanity’s vision for safer, efficient, and accessible transportation systems across the world. Due to the multitude of data sources and processes associated with Intelligent vehicles, the reliability of the total system is greatly dependent on the possibility of errors or poor performances observed in its components. In our work, we focus on the critical task of localization of intelligent vehicles and address the challenges in monitoring the integrity of data sources used in localization. The primary contribution of our research is the proposition of a novel protocol for integrity by combining integrity concepts from information systems with the existing integrity concepts in the field of Intelligent Transport Systems (ITS). An integrity monitoring framework based on the theorized integrity protocol that can handle multimodal localization problems is formalized. As the first step, a proof of concept for this framework is developed based on cross-consistency estimation of data sources using polynomial models. Based on the observations from the first step, a 'Feature Grid' data representation is proposed in the second step and a generalized prototype for the framework is implemented. The framework is tested in highways as well as complex urban scenarios to demonstrate that the proposed framework is capable of providing continuous integrity estimates of multimodal data sources used in intelligent vehicle localization
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Amini, Arghavan. "An Integrated and a smart algorithm for vehicle positioning in intelligent transportation systems." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/47463.
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Intelligent Transportation Systems (ITS) have emerged to use different technologies to promote safety, convenience, and efficiency of transportation networks. Many applications of ITS depend on the availability of the real-time positioning of the vehicles in the network. In this research, the two open challenges in the field of vehicle localization for ITS are introduced and addressed.
First, in order to have safe and efficient transportation systems, the locations of the vehicles need to be available everywhere in a network. Conventional localization techniques mostly rely on Global Positioning System (GPS) technology which cannot meet the accuracy requirements for all applications in all situations. This work advances the study of vehicle positioning in ITS by introducing an integrated positioning framework which uses several resources including GPS, vehicle-to-infrastructure and vehicle-to-vehicle communications, radio-frequency identification, and dead reckoning. These technologies are used to provide more reliable and accurate location information. The suggested framework fills the gap between the accuracy of the current vehicle localization techniques and the required one for many ITS applications.
Second, different ITS applications have different localization accuracy and latency requirements. A smart positioning algorithm is proposed which enable us to change the positioning accuracy delivered by the algorithm based on different applications. The algorithm utilizes only the most effective resources to achieve the required accuracy, even if more resources are available. In this way, the complexity of the system and the running time decrease while the desired accuracy is obtained. The adjective Smart is selected because the algorithm smartly selects the most effective connection which has the most contribution to vehicle positioning when a connection needs to be added. On the other hand, when a connection should be removed, the algorithm smartly selects the least effective one which has the least contribution to the position estimation.
This study also provides an overview about the positioning requirements for different ITS applications. A close-to-real-world scenario has been developed and simulated in MATLAB to evaluate the performance of the proposed algorithms. The simulation results show that the vehicle can acquire accurate location in different environments using the suggested Integrated framework. Moreover, the advantages of the proposed Smart algorithm in terms of accuracy and running time are presented through a series of comprehensive simulations.Master of Science
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Qiao, Yongliang. "Place recognition based visual localization in changing environments." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCA004/document.
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Dans de nombreuses applications, il est crucial qu'un robot ou un véhicule se localise, notamment pour la navigation ou la conduite autonome. Cette thèse traite de la localisation visuelle par des méthodes de reconnaissance de lieux. Le principe est le suivant: lors d'une phase hors-ligne, des images géo-référencées de l'environnement d'évolution du véhicule sont acquises, des caractéristiques en sont extraites et sauvegardées. Puis lors de la phase en ligne, il s'agit de retrouver l'image (ou la séquence d'images) de la base d'apprentissage qui correspond le mieux à l'image (ou la séquence d'images) courante. La localisation visuelle reste un challenge car l'apparence et l'illumination changent drastiquement en particulier avec le temps, les conditions météorologiques et les saisons. Dans cette thèse, on cherche alors à améliorer la reconnaissance de lieux grâce à une meilleure capacité de description et de reconnaissance de la scène. Plusieurs approches sont proposées dans cette thèse:1) La reconnaissance visuelle de lieux est améliorée en considérant les informations de profondeur, de texture et de forme par la combinaison de plusieurs de caractéristiques visuelles, à savoir les descripteurs CSLBP (extraits sur l'image couleur et l'image de profondeur) et HOG. De plus l'algorithme LSH (Locality Sensitive Hashing) est utilisée pour améliorer le temps de calcul;2) Une méthode de la localisation visuelle basée sur une reconnaissance de lieux par mise en correspondance de séquence d'images (au lieu d'images considérées indépendamment) et combinaison des descripteurs GIST et CSLBP est également proposée. Cette approche est en particulier testée lorsque les bases d'apprentissage et de test sont acquises à des saisons différentes. Les résultats obtenus montrent que la méthode est robuste aux changements perceptuels importants;3) Enfin, la dernière approche de localisation visuelle proposée est basée sur des caractéristiques apprises automatiquement (à l'aide d'un réseau de neurones à convolution) et une mise en correspondance de séquences localisées d'images. Pour améliorer l'efficacité computationnelle, l'algorithme LSH est utilisé afin de viser une localisation temps-réel avec une dégradation de précision limitéeIn many applications, it is crucial that a robot or vehicle localizes itself within the world especially for autonomous navigation and driving. The goal of this thesis is to improve place recognition performance for visual localization in changing environment. The approach is as follows: in off-line phase, geo-referenced images of each location are acquired, features are extracted and saved. While in the on-line phase, the vehicle localizes itself by identifying a previously-visited location through image or sequence retrieving. However, visual localization is challenging due to drastic appearance and illumination changes caused by weather conditions or seasonal changing. This thesis addresses the challenge of improving place recognition techniques through strengthen the ability of place describing and recognizing. Several approaches are proposed in this thesis:1) Multi-feature combination of CSLBP (extracted from gray-scale image and disparity map) and HOG features is used for visual localization. By taking the advantages of depth, texture and shape information, visual recognition performance can be improved. In addition, local sensitive hashing method (LSH) is used to speed up the process of place recognition;2) Visual localization across seasons is proposed based on sequence matching and feature combination of GIST and CSLBP. Matching places by considering sequences and feature combination denotes high robustness to extreme perceptual changes;3) All-environment visual localization is proposed based on automatic learned Convolutional Network (ConvNet) features and localized sequence matching. To speed up the computational efficiency, LSH is taken to achieve real-time visual localization with minimal accuracy degradation
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Li, Hao. "Cooperative perception : Application in the context of outdoor intelligent vehicle systems." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://pastel.archives-ouvertes.fr/pastel-00766986.
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The research theme of this dissertation is the multiple-vehicles cooperative perception (or cooperative perception) applied in the context of intelligent vehicle systems. The general methodology of the presented works in this dissertation is to realize multiple-intelligent vehicles cooperative perception, which aims at providing better vehicle perception result compared with single vehicle perception (or non-cooperative perception). Instead of focusing our research works on the absolute performance of cooperative perception, we focus on the general mechanisms which enable the realization of cooperative localization and cooperative mapping (and moving objects detection), considering that localization and mapping are two underlying tasks for an intelligent vehicle system. We also exploit the possibility to realize certain augmented reality effect with the help of basic cooperative perception functionalities; we name this kind of practice as cooperative augmented reality. Naturally, the contributions of the presented works consist in three aspects: cooperative localization, cooperative local mapping and moving objects detection, and cooperative augmented reality.
Books on the topic "Intelligent vehicles localization"
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Wishart, Jeffrey, Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu, and Yezhou Yang. Fundamentals of Connected and Automated Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/9780768099829.
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The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.
Book chapters on the topic "Intelligent vehicles localization"
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Rovira Más, Francisco, Qin Zhang, and Alan C. Hansen. "Three-dimensional Perception and Localization." In Mechatronics and Intelligent Systems for Off-road Vehicles, 111–85. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-468-5_5.
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Freitas, Gustavo, Ji Zhang, Bradley Hamner, Marcel Bergerman, and George Kantor. "A Low-Cost, Practical Localization System for Agricultural Vehicles." In Intelligent Robotics and Applications, 365–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33503-7_36.
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Lin, Cheng, and Wanmi Chen. "Research on Basketball Robot Recognition and Localization Based on MobileNet-SSD and Multi-sensor." In Intelligent Equipment, Robots, and Vehicles, 55–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7213-2_6.
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Li, Liang, Ming Yang, Lindong Guo, Chunxiang Wang, and Bing Wang. "Precise and Reliable Localization of Intelligent Vehicles for Safe Driving." In Intelligent Autonomous Systems 14, 1103–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48036-7_81.
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Masselli, Andreas, Richard Hanten, and Andreas Zell. "Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images." In Intelligent Autonomous Systems 13, 831–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_60.
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Wang, Ju, Hongzhe Liu, Hong Bao, Brian Bennett, and Cesar Flores-Montoya. "Target Localization and Navigation with Directed Radio Sensing in Wireless Sensor Networks." In Internet of Vehicles - Safe and Intelligent Mobility, 101–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27293-1_10.
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Hussein, Hanan H., Mohamed Hanafy Radwan, and Sherine M. Abd El-Kader. "Proposed Localization Scenario for Autonomous Vehicles in GPS Denied Environment." In Advances in Intelligent Systems and Computing, 608–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58669-0_55.
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Idhis, Sally M., Takwa Dawdi, Qassim Nasir, Manar Abu Talib, and Yara Omran. "Detection and Localization of Unmanned Aerial Vehicles Based on Radar Technology." In Advanced Computing and Intelligent Technologies, 429–52. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2164-2_34.
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Skrzypczyński, Piotr. "LiDAR Localization and Mapping for Autonomous Vehicles: Recent Solutions and Trends." In Advances in Intelligent Systems and Computing, 251–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74893-7_24.
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Yan, Yuheng, Haojie Zheng, and Jian Xiao. "Cooperative Localization System of Unmanned Aerial Vehicles by UWB System and GNSS Sensors." In Advances in Intelligent Automation and Soft Computing, 918–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_105.
Full textConference papers on the topic "Intelligent vehicles localization"
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Badino, H., D. Huber, and T. Kanade. "Visual topometric localization." In 2011 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2011. http://dx.doi.org/10.1109/ivs.2011.5940504.
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Heirich, Oliver, Patrick Robertson, Adrian Cardalda Garcia, Thomas Strang, and Andreas Lehner. "Probabilistic localization method for trains." In 2012 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2012. http://dx.doi.org/10.1109/ivs.2012.6232194.
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Wu, Tao, and Ananth Ranganathan. "Vehicle localization using road markings." In 2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013. http://dx.doi.org/10.1109/ivs.2013.6629627.
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Ziegler, Julius, Henning Lategahn, Markus Schreiber, Christoph G. Keller, Carsten Knoppel, Jochen Hipp, Martin Haueis, and Christoph Stiller. "Video based localization for Bertha." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856560.
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Peker, Ali Ufuk, Tankut Acarman, Cafdas Yaman, and Erkan Yuksel. "Vehicle localization enhancement with VANETs." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856576.
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Ghods, Alireza, Stefano Severi, and Giuseppe Abreu. "Localization in V2X communication networks." In 2016 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2016. http://dx.doi.org/10.1109/ivs.2016.7535355.
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Ndjeng, Alexandre Ndjeng, Sebastien Glaser, and Dominique Gruyer. "A Multiple Model Localization System for Outdoor Vehicles." In 2007 IEEE Intelligent Vehicles Symposium. IEEE, 2007. http://dx.doi.org/10.1109/ivs.2007.4290255.
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Wu, Shun-xi, and Ming Yang. "Landmark Pair based Localization for Intelligent Vehicles using Laser Radar." In 2007 IEEE Intelligent Vehicles Symposium. IEEE, 2007. http://dx.doi.org/10.1109/ivs.2007.4290116.
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Gruyer, Dominique, Rachid Belaroussi, and Marc Revilloud. "Map-aided localization with lateral perception." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856528.
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Wong, David, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, and Hiroshi Murase. "Monocular localization within sparse voxel maps." In 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017. http://dx.doi.org/10.1109/ivs.2017.7995767.
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