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5G Networks Enabling Cooperative Autonomous vehicle Localization: A Survey

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 15291-15313页

作者： Zhou, Bei Liu, Zhitao Su, Hongye Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

Precise and real-time localization is the key for autonomous vehicles (Avs) to solve traffic, environmental problems and support intelligent vehicle applications. Existing Av localization methods typically rely on the cooperation between onboard sensors, cellular systems, and vehicular networks to satisfy the high localization requirements. The advent of 5G networks can not only revolutionize cellular localization performance but also provide adept capacity to handle massive sensor data and dependable vehicle-to-everything (v2X) transmission at impressive speeds with minimal latency. These advancements stir expectations to promote cooperative Av localization from lane-level accuracy to centimeter-level. The purpose of this survey is to elaborate on the predominance of applying 5G networks for cooperative Av localization, which overviews 5G new radio features, 5G localization techniques, and the pivotal algorithms supporting 5G Av localization, including filtering-based and machine learning algorithms. Three applications of 5G cooperative Av localization are highlighted, respectively the integrations of 5G networks with satellite systems, simultaneous localization and mapping algorithms, and vehicular networks. Moreover, this survey analyzes the challenges in existing 5G cooperative Av localization systems and summarizes the potential research hotspots for further development.

关键词： Location awareness 5G mobile communication Surveys Accuracy sensors vehicle-to-everything Topology 5G networks autonomous vehicles cooperative localization Internet of vehicles sensor fusion

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A Modular sensor fusion and Quality-Driven sensor Selection Algorithm for UAv Navigation 20th

A Modular Sensor Fusion and Quality-Driven Sensor Selection ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Chen, Hainuo Ni, Xuebin Gao, Ming Ma, Hui Zhang, Chen Yunsheng Intelligent Technol Co Ltd Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Tianjin 300457 Peoples R China

ISBN: (纸本)9789819755936;9789819755943

Unmanned Aerial vehicles (UAvs) are increasingly integrated in various applications. The urgent need for accurate navigation algorithms is emphasized in various applications. In this paper, we propose an integrated framework focusing on robust attitude and position/velocity estimation for UAv navigation using Kalman filtering employing Kalman filtering (KF) technique and chi-square test based sensor fusion quality assessment. Our approach integrates sensor data through a filter-based approach, and Kalman filter handles attitude estimation and position/velocity estimation. The modal architecture enhances robustness and facilitates seamless integration of multiple sensors while prioritizing reduced attitude estimation and position/velocity estimation. Accurate attitude estimation is also prioritized to reduce the risk in case of deviation. Accurate attitude estimation is also prioritized to reduce the risk of deviation in case of higher order state estimators. Experimental validation on the "SHENG PROUAv" model proves the effectiveness of our framework and demonstrates accurate attitude estimation results.

关键词： Integrated Navigation Systems Modular sensor fusion Kalman Filters Fault Detection

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Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches

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INFORMATION fusion 2023年第1期95卷 62-90页

作者： Zhuang, Yuan Sun, Xiao Li, You Huai, Jianzhu Hua, Luchi Yang, Xiansheng Cao, Xiaoxiang Zhang, Peng Cao, Yue Qi, Longning Yang, Jun El-Bendary, Nashwa El-Sheimy, Naser Thompson, John Chen, Ruizhi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China Southeast Univ Natl ASIC Ctr Nanjing Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Arab Acad Sci Technol & Maritime Transport AASTMT Aswan Egypt Univ Calgary Dept Geomat Engn Calgary AB Canada Univ Edinburgh Sch Engn Edinburgh Scotland

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial vehicle (UAv), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors;consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi -sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed.

关键词： Machine learning Data fusion Estimation Integrated navigation system Multi-sensor Positioning

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Data stream fusion for accurate quantile tracking and analysis

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INFORMATION fusion 2023年 89卷 155-165页

作者： Cafaro, Massimo Melle, Catiuscia Epicoco, Italo Pulimeno, Marco Univ Salento Lecce Italy

UDDSketch is a recent algorithm for accurate tracking of quantiles in data streams, derived from the DDSketch algorithm. UDDSketch provides accuracy guarantees covering the full range of quantiles independently of the input distribution and greatly improves the accuracy with regard to DDSketch. In this paper we show how to compress and fuse two or more data streams (or datasets) by leveraging the mergeability of the UDDSketch data summaries. In general, two summaries on two data streams are said to be mergeable if there exists an algorithm that allows combining the two summaries into a single one related to the union of the two datasets, simultaneously preserving the error and size guarantees. The property of mergeability of a sketch enables the parallel and distributed processing of big volume data streams that can be compressed and fused by means of such mergeable data structures. Among the applications strictly related to accurate tracking of quantiles, requiring parallel and/or distributed processing we recall here estimating the latency of a web site, database query optimizers and the need of succinctly summarizing the distribution of values occurring over a sensor network. We prove that UDDSketch is fully mergeable and introduce PUDDSketch, a parallel version of UDDSketch suitable for message-passing based architectures. We formally prove its correctness and compare it to a parallel version of DDSketch, showing through extensive experimental results that our parallel algorithm almost always outperforms the parallel DDSketch algorithm with regard to the overall accuracy in determining the quantiles. Moreover, we also design and implement parallel versions of both the state of the art KLL and REQ sequential algorithms in order to compare and contrast PUDDSketch versus the corresponding parallel algorithms. Our experiments clearly show that PUDDSketch is faster or on par with regard to parallel running time, whilst providing simultaneously greater accuracy.

关键词： Quantiles Sketches Message-passing

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A hybrid high speed eye tracking sensor by fusion of video oculography and laser feedback interferometry 5

A hybrid high speed eye tracking sensor by fusion of video o...

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Optical architectures for Displays and Sensing in Augmented, virtual, and Mixed Reality (AR, vR, MR) v 2024

作者： Meyer, Johannes Mühlbauer, Michael Corporate Sector Research and Advance Engineering Dpt. of Advanced Technologies and Micro Systems Optical Microsystems Robert Bosch GmbH Renningen Germany

ISBN: (纸本)9781510670860

High-speed eye tracking is a key requirement for upcoming AR and vR devices, as it enables novel applications such as display enhancement through gaze-contingent rendering, saccadic endpoint prediction to minimize system latency, and gaze-based biometric user identification. The update rate of state-of-the-art mobile video oculography sensors is limited by system-level power consumption, which is mainly caused by the exponentially increasing computational power requirements for pupil detection in camera images with respect to the update rate. To overcome this limitation and enable unconstrained high-speed eye tracking with low power consumption, we propose to fuse eye movement velocity data acquired by laser feedback interferometry sensors with camera images acquired at a low sampling rate. We propose a model-based sensor fusion approach that involves a combination of a glint-free, model-based video-oculography approach and high-speed interpolation between successive camera images using velocity information derived from the laser feedback interferometry sensors to achieve an outstanding eye tracking sensor system update rate of 1 kHz. We evaluated the proposed hybrid sensor system in an experimental setup and achieved a gaze accuracy of 1.63°. © 2024 SPIE.

关键词： Eye tracking

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A novel road attribute detection system for autonomous vehicles using sensor fusion

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International Journal of Information Technology (Singapore) 2025年第1期17卷 161-168页

作者： Thomas, Anoop Antony, Jobin K. Isaac, Ashish v. Aromal, M.S. verghese, Sam Department of Electronics and Communication Engineering Rajagiri School of Engineering and Technology Kochi India Affiliated to APJ Abdul Kalam Technological University Thiruvananthapuram India

The development of Society of Automotive Engineers (SAE) Level 5 Autonomous vehicles (Avs), which are capable of navigating a variety of roads and weather situations on their own, is examined in this study. Through the application of sophisticated computational algorithms, such as Improved You Only Look Once (YOLO) v5, Single Shot multibox Detector (SSD), Mask- Region based Convolutional Neural Networks (RCNN), and Nanodet, which are based on Convolutional Neural Networks (CNN), the research aims to enhance perception, prediction, and decision-making for secure and efficient autonomous navigation. The focus is on detecting road attributes like humps and potholes. Comparative analysis is carried out on both standard and custom datasets, leading to the selection of algorithms for real-time implementation. The proposed system employs a high-resolution camera mounted on a vehicle, connected to Graphics Processing Unit (GPU) accelerated embedded board, and implemented on the Robot Operating System (ROS) based software platform. Data collected on a specific route serves as valuable input for autonomous navigation. Additionally, the paper delves into the fusion of camera and Light Detection and Ranging (LiDAR) sensor data, introducing novel software architecture to seamlessly integrate road attribute detection into existing Av navigation pipelines on the ROS platform. © Bharati vidyapeeth's Institute of Computer applications and Management 2024.

关键词： Camera COCO dataset LiDAR Mask-RCNN Nanodet ROS SAE SSD YOLO

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Robust sensor fusion algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan Lokeshwari, Gandrakoti Jagadeesan, S. Leela, S. Pratheep, v.G. venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

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A Distributed Storage and fusion Management Method of Massive vehicle Multi-Source sensor Data 4

A Distributed Storage and Fusion Management Method of Massiv...

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4th International Conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Wang, Meng Ji, Yuan Qian, Junfeng Xia, Wei Ye, Haoting Guizhou Power Grid Co Ltd Guizhou China Co. Ltd Guangdong China The Chinese University of Hong Kong Shenzhen Shenzhen China

ISBN: (纸本)9798331531409

With the advancement of sensor technology, an increasing amount of multi-source vehicle sensor data has emerged, offering significant value in areas such as high-precision navigation and intelligent driving. In this article, a fused organization expression model for vehicle multi-source sensor data is proposed, name v-MSFOEM. Based on the v-MSFOEM and combined with a distributed database, query algorithms such as spatial nearest-neighbor point query, spatio-temporal range query, and fused point data based on interest sections are proposed. The public dataset KITTI is used to compare the proposed algorithm with the traditional query methods, and the experimental results demonstrate that the proposed algorithms exhibit higher query efficiency, providing flexible and efficient foundational data support for upper-layer applications. © 2024 IEEE.

关键词： Structured Query Language

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Non-Invasive Human-Machine Interface (HMI) Systems With Hybrid On-Body sensors for Controlling Upper-Limb Prosthesis: A Review

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IEEE sensorS JOURNAL 2022年第11期22卷 10292-10307页

作者： Zhou, Hao Alici, Gursel Univ Wollongong ARC Ctr Excellence Electromat Sci Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia

In this work, we present a systematic review on non-invasive HMIs employing hybrid wearable sensor modalities for recognition of upper limb intentions. Different combinations of the sensors are investigated. As sEMG is dominant in the applications of externally powered prosthetic hands, it is involved in most hybrid sensor combinations. The combined use of sEMG and IMU is most studied in the literature as IMU is easy to be integrated. Though limited, the investigation on other hybrid modalities has been drawing more and more research attention and efforts, especially those with FMG and NIRS. For all the reported hybrid sensors, it is verified that this strategy can enrich the information of user intention and help the pattern recognition and/or intensity regulation of robotic hand/arm prosthesis. Though it is the trend, the development of these hybrid-sensor-based HMIs are still at the preliminary stage. More dedicated sensor fusion models and system architectures as well as new hybrid features and algorithms need to be developed to make the best use of each sensing modality's strength to achieve robust and stable user intention recognition, which is essential for the progress and user acceptance of upper limb prosthesis.

关键词： sensors Muscles Robot sensing systems Robustness Electroencephalography Biosensors sensor systems Human-machine-interface HMI upper limb prosthesis hybrid modalities multi-modal sensor fusion sEMG plus MMG sEMG plus FMG sEMG plus SMG sEMG plus NIRS robustness adaptive learning

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Edge computing in the loop simulation framework for automotive use cases evaluation

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WIRELESS NETWORKS 2023年第8期29卷 3717-3735页

作者： Maller, Levente Mark Suskovics, Peter Bokor, Laszlo Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Networked Syst & Serv Muegyetem Rkp 3 H-1111 Budapest Hungary Ericsson Digital Serv Technol & Innovat Magyar Tudosok Korutja 11 H-1117 Budapest Hungary ELKH BME Cloud Applicat Res Grp Magyar Tudosok Krt 2 H-1117 Budapest Hungary

Edge architectures provide local, decentralized services, enabling balancing network traffic and distributing hardware resources. Later, many new use cases can be implemented by combining the advantages of the edge computing concept with the services of 5G systems. One of the biggest beneficiaries of this could be the vehicle-to-Cloud (v2C) technology, where it is necessary to efficiently process large amounts of data resulting from vehicle-to-Everything communication (v2X) services. In specific use cases, this makes it possible to process sensor data collectively, enhanced by fusion, which promotes a more effective virtual representation of the real world. The effective implementation of these technologies is a complex task. One of the most important steps before tests on actual infrastructures with real vehicles is evaluating and validating edge cloud systems. We present a solution for this problem, the Cloud-in-the-Loop (CiL) simulation framework. It can orchestrate a real-size, telco-grade level, Kubernetes-based edge cloud infrastructure based on information gathered from a traffic simulator and performing fine-grained benchmarking and data collection. In addition to the performance analysis of the edge system, it also enables an in-depth examination of cloud-native applications serving complex automotive use cases. In this paper, we focus on presenting the developed framework and its capabilities by utilizing the system with implemented test applications, and give an example of testing QoS and QoE aspects of the edge cloud-based v2C concept.

关键词： Edge cloud Automotive use cases Cloud-in-the-loop simulation Kubernetes Cloud-native applications 5G Telco cloud

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