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Communication, Positioning, and Sensing Solutions for Autonomous Vehicles

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 22866

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Special Issue Editors

Prof. Dr. Elena Simona Lohan

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Guest Editor

Tampere University (TAU), P.O.Box 553, FIN-33101 Tampere, Finland
Interests: wireless localization; tracking and navigation; signal processing for wireless communications; wearable computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mikko Valkama

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Guest Editor

Electrical Engineering unit, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland
Interests: Communications Signal Processing, I/Q Signal Processing, Dirty-RF Signal Processing and Radio Architectures, Signal Processing Algorithms for Software Defined Flexible Radios, 5G

Prof. Dr. Riku Jantti

E-Mail Website
Guest Editor

Department of Communications and Networking, School of Electrical Engineering, Aalto University, Otakaari 1B, FI-00076 AALTO, Espoo, Finland
Interests: radio resource control and optimization for machine type communications, Cloud based Radio Access Networks, RF Inference, and quantum communications

Dr. Claudio Roncoli

E-Mail Website
Guest Editor

Department of Built Environment, School of Engineering, Aalto University, Otakaari 1B, FI-00076 AALTO, Espoo, Finland
Interests: optimization and control of traffic systems, macroscopic and microscopic traffic flow modelling, real-time monitoring of large-scale transportation systems, intelligent transportation systems

Special Issue Information

Dear Colleagues,

The use of autonomous robots in various industries, such as the industrial internet, automated shipping, automated maritime vessels, or autonomous driving, is increasing steadily. The global autonomous driving market is expected to grow to more than EUR 150 billion by 2035 and the shared mobility services will form more than 65% of this market. The industrial internet market is expected to rely on more than 60 billion connected devices in the next five years, which will require accurate and highly available positioning and sensing mechanisms to ensure integrated indoor–outdoor deployments for moving connected robots in 3D spaces. To achieve ultra-reliable and low-latency wireless communications without a significant increase in complexity, these autonomous systems of tomorrow need enhanced low-cost and low-energy-consumption solutions for autonomous localization, location-aware communications, and sensing, for example by re-using already available communication signals.

This Special Issue encourages authors from academia and industry to submit new research results about technological innovations and novel applications for communication, sensing, and navigation solutions for autonomous machines, robots, and drones. The Special Issue topics include but are not limited to:

Wireless positioning, localization, and navigation for autonomous robots and devices
Wireless communication solutions for autonomous machines, robots, and/or drones
Novel algorithms for swarms of robots and/or drones
Interference localization, detection, and mitigation in wireless communications and wireless navigation of autonomous vehicles
Enhanced situational awareness and environmental mapping solutions with cellular/5G/beyond 5G and IoT solutions
5G/B5G-based localization and sensing solutions
mmWave communications, localization, and sensing solutions
Testbeds/pilots measurement data analysis for autonomous everything
RF fingerprinting for transmitter and receiver identification/authentication in safe and reliable autonomous connectivity

Dr. Elena Simona Lohan
Prof. Dr. Mikko Valkama
Prof. Dr. Riku Jäntti
Dr. Claudio Roncoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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16 pages, 488 KiB

Open AccessArticle

A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station

by Bo Sun, Bo Tan, Wenbo Wang and Elena Simona Lohan

Sensors 2021, 21(4), 1178; https://doi.org/10.3390/s21041178 - 8 Feb 2021

Cited by 19 | Viewed by 3923

Abstract

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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20 pages, 9008 KiB

Open AccessArticle

A Highly Accurate Positioning Solution for C-V2X Systems

by Qi Liu, Peng Liang, Junjie Xia, Ti Wang, Meng Song, Xingrong Xu, Jiachi Zhang, Yuanyuan Fan and Liu Liu

Sensors 2021, 21(4), 1175; https://doi.org/10.3390/s21041175 - 7 Feb 2021

Cited by 17 | Viewed by 4536

Abstract

Cellular vehicle-to-everything (C-V2X) is essential in enabling safe, reliable, and efficient transportation services. It serves as serve as the foundation for vehicles to communicate with each other and everything around them. One fundamental element in C-V2X is positioning, namely extracting the vehicle’s absolute and relative positions concerning other objects such as buildings, pedestrians, traffic signs, and other vehicles. However, its feasibility in enabling vehicular positioning has not been fully explored yet. In this paper, key performance indicators (KPIs) for C-V2X positioning have been described firstly. Then positioning challenges and conventional positioning methods for C-V2X are reviewed. Afterward, two user equipment (UE)-based and UE-assisted C-V2X positioning architectures are proposed, and key technologies are also described. Lastly, testing and typical application cases are provided. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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22 pages, 2781 KiB

Open AccessArticle

Feasibility of Location-Aware Handover for Autonomous Vehicles in Industrial Multi-Radio Environments

by Yi Lu, Mikhail Gerasimenko, Roman Kovalchukov, Martin Stusek, Jani Urama, Jiri Hosek, Mikko Valkama and Elena Simona Lohan

Sensors 2020, 20(21), 6290; https://doi.org/10.3390/s20216290 - 5 Nov 2020

Cited by 9 | Viewed by 2470

Abstract

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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21 pages, 4731 KiB

Open AccessArticle

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

by Sorin Grigorescu, Tiberiu Cocias, Bogdan Trasnea, Andrea Margheri, Federico Lombardi and Leonardo Aniello

Sensors 2020, 20(19), 5450; https://doi.org/10.3390/s20195450 - 23 Sep 2020

Cited by 9 | Viewed by 4092

Abstract

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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19 pages, 2445 KiB

Open AccessArticle

A Novel Time Delay Estimation Algorithm for 5G Vehicle Positioning in Urban Canyon Environments

by Zhongliang Deng, Xinyu Zheng, Hanhua Wang, Xiao Fu, Lu Yin and Wen Liu

Sensors 2020, 20(18), 5190; https://doi.org/10.3390/s20185190 - 11 Sep 2020

Cited by 12 | Viewed by 2821

Abstract

Vehicle positioning with 5G can effectively compensate for the lack of vehicle positioning based on GNSS (Global Navigation Satellite System) in urban canyons. However, there is also a large ranging error in the non-line of sight (NLOS) propagation of 5G. Aiming to solve this problem, we consider a new time delay estimation algorithm called non-line of sight cancellation multiple signal classification (NC-MUSIC). This algorithm uses cross-correlation to identify and cancel the NLOS signal. Then, an unsupervised multipath estimation method is used to estimate the number of multipaths and extract the noise subspace. The MUSIC spectral function can be calculated by the noise subspace. Finally, the time delay of the direct path is estimated by searching the peak of MUSIC spectral function. This paper adopts the 5G channel model developed by 3GPP TR38.901 for simulation experiments. The experiment results demonstrated that the proposed algorithm has obvious advantages in terms of NLOS propagation for urban canyon environments. It provided a high-precision time delay estimation algorithm for observed time difference of arrival (OTDOA), joint angle of arrival (AOA) ranging, and other positioning methods in the 5G vehicle positioning method, which can effectively improve the positioning accuracy of 5G vehicle positioning in urban canyon environments. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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20 pages, 5428 KiB

Open AccessArticle

A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation

by Qifeng Lai, Hong Yuan, Dongyan Wei, Ningbo Wang, Zishen Li and Xinchun Ji

Sensors 2020, 20(9), 2551; https://doi.org/10.3390/s20092551 - 30 Apr 2020

Cited by 6 | Viewed by 2481

Abstract

Using the Global Navigation Satellite System (GNSS), it is difficult to provide continuous and reliable position service for vehicle navigation in complex urban environments, due to the natural vulnerability of the GNSS signal. With the rapid development of the sensor technology and the reduction in their costs, the positioning performance of GNSS is expected to be significantly improved by fusing multi-sensors. In order to improve the continuity and reliability of the vehicle navigation system, we proposed a multi-sensor tight fusion (MTF) method by combining the inertial navigation system (INS), odometer, and barometric altimeter with the GNSS technique. Different fusion strategies were presented in the open-sky, insufficient satellite, and satellite outage environments to check the performance improvement of the proposed method. The simulation and real-device tests demonstrate that in the open-sky context, the error of sensors can be estimated correctly. This is useful for sensor noise compensation and position accuracy improvement, when GNSS is unavailable. In the insufficient satellite context (6 min), with the help of the barometric altimeter and a clock model, the accuracy of the method can be close to that in the open-sky context. In the satellite outage context, the error divergence of the MTF is obviously slower than the traditional GNSS/INS tightly coupled integration, as seen by odometer and barometric altimeter assisting. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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Other

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16 pages, 414 KiB

Open AccessLetter

Vehicular Localization Enhancement via Consensus

by Hong Ki Kim, Minji Kim and Sang Hyun Lee

Sensors 2020, 20(22), 6506; https://doi.org/10.3390/s20226506 - 14 Nov 2020

Cited by 1 | Viewed by 1457

Abstract

This paper presents a strategy to cooperatively enhance the vehicular localization in vehicle-to-everything (V2X) networks by exchanges and updates of local data in a consensus-based manner. Where each vehicle in the network can obtain its location estimate despite its possible inaccuracy, the proposed strategy takes advantage of the abundance of the local estimates to improve the overall accuracy. During the execution of the strategy, vehicles exchange each other’s inter-vehicular relationship pertaining to measured distances and angles in order to update their own estimates. The iteration of the update rules leads to averaging out the measurement errors within the network, resulting in all vehicles’ localization error to retain similar magnitudes and orientations with respect to the ground truth locations. Furthermore, the estimate error of the anchor—the vehicle with the most reliable localization performance—is temporarily aggravated through the iteration. Such circumstances are exploited to simultaneously counteract the estimate errors and effectively improve the localization performance. Simulated experiments are conducted in order to observe the nature and its effects of the operations. The outcomes of the experiments and analysis of the protocol suggest that the presented technique successfully enhances the localization performances, while making additional insights regarding performance according to environmental changes and different implementation techniques. Full article

(This article belongs to the Special Issue Communication, Positioning, and Sensing Solutions for Autonomous Vehicles)

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