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IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 57365

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Special Issue Editors

Prof. Dr. Sergio Saponara

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Department of Information Engineering, University of Pisa, Via Girolamo Caruso, 16, 56122 Pisa, Italy
Interests: automotive electronics; embedded HPC (high-performance computing); enabling technologies IoT (Internet of Things)
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Stefano Giordano

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Dipartimento di Ingegneria della Informazione (DII), Università di Pisa, Via G. Caruso 16, 56122 Pisa, Italy
Interests: industrial IoT; agriculture; tactile internet; tele surgery
Special Issues, Collections and Topics in MDPI journals

Dr. Riccardo Mariani

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Riccardo Mariani, NVIDIA, Santa Clara, CA 95050, USA
Interests: functional safety; integrated circuits reliability; IoT (Internet of Things); ISO26262

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Sensors entitled “IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles”.
Application of IoT (Internet of Things) technologies is revolutionizing the mobility of people and also goods (i.e., logistics).
The widespread diffusion of distributed and networked sensing and control systems makes possible the set-up of intelligent transport systems (ITS) that are able to monitor, predict, and manage traffic flows. Considering also the possibility of remote vehicle fleet management, and the availability of a sensorized and 5G-connected infrastructure, it will be possible to reduce pollution emissions, time lost in traffic jams, and the number of people killed or injured in accidents. On the other hand, vehicles for both traffic and private transportation (i.e., automotive, bus, coaches, and trains) will become increasingly automated, connected, and shared.
Key items for such a revolution are sensing systems for machine and infrastructure perception and networking technologies for V2I (vehicle-to-infrastructure), V2V (vehicle-to-vehicle), and V2P (vehicle-to-pedestrians) communication. On the sensing side, new contactless techniques are appearing from physical sensors based on light, electromagnetic, or acoustics waves (i.e., videocameras, Lidar, Radar, and ultrasonics) to social sensors exploiting mobile connectivity and web activity of people. Sensor fusion and data understanding, thanks to the adoption of machine learning (ML) and artificial intelligence (AI) techniques, are new emerging research fields. On the networking side, the advent of the 5G era and of edge/cloud-computing paradigms will present new opportunities in terms of increased data-rate, link density, and quality of service, and reduced latency (i.e., tactile internet) for communication, localization, navigation, and tracking.
Since the mobility of people and goods is a safe critical application, functional safety is also a key technology. Another key technology is the cybersecurity to be integrated, via HW and/or SW solutions, in vehicles, in infrastructure and in relevant interconnections.

The particular topics of interest for this Special Issue include but are not limited to the following:

New technologies for sensors and for networking transceivers (RF, photonics, and MEMS/MOEMS);
V2X (vehicle to everything) and 5G networking technologies;
Advances in social sensors;
Advances in physical sensors (lidar, radar, videocameras, ultrasonics);
New applications and services for mobility of people and goods and for smart cities infrastructure;
Edge and cloud paradigms for sensing systems, localization/navigation, and vehicular networks;
Enabling technologies for traffic monitoring and fleet management;
Cybersecurity/trust/privacy in IoT sensing systems and vehicular networks;
Functional safety and standardization (e.g., ISO26262);
Data analytics and machine learning for sensors fusion and vehicular networks;
Embedded systems and HPC architectures for sensing and vehicular networks;
Modeling, optimization, and performance evaluation of vehicular networks;
Simulation of autonomous connected vehicles and mobility traffic;
Results from experimental systems and prototypes in industry and academia.

Most of the above topics are also key enabling technologies within industry 4.0 and robotic scenarios, and hence paper submissions related to these domains are also invited. The proposed Special Issue and the relevant topics are part of the activities of the Crosslab Industrial IoT carried out by DII-University of Pisa in the framework of the “Dipartimenti di Eccellenza” project.

Prof. Dr. Sergio Saponara
Prof. Dr. Stefano Giordano
Dr. Riccardo Mariani
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.

Keywords

New technologies for sensors and for networking transceivers (RF, photonics, MEMS/MOEMS)
V2X and 5G networking technologies
Advances in social sensors
Advances in physical sensors (lidar, radar, videocameras, and ultrasonics)
New applications and services for mobility, logistics and smart cities
Edge and cloud paradigms for sensing systems, localization/navigation and vehicular networks
Enabling technologies for traffic monitoring and fleet management
Cybersecurity/trust/privacy in IoT sensing systems and vehicular networks
Functional safety and standardization (e.g., ISO26262)
Gas emission reduction and energy efficiency opportunities with traffic and vehicle management
Data analytics and machine learning for sensors fusion and vehicular networks
Embedded systems and HPC architectures for sensing and vehicular networks
Modeling, simulation, optimization, and performance evaluation of vehicular networks and traffic
Results from experimental systems and prototypes in industry and academia
Applications to vehicles, industry 4.0, robotics, and smart cities

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Published Papers (12 papers)

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Editorial

Jump to: Research

5 pages, 184 KiB

Open AccessEditorial

Recent Trends on IoT Systems for Traffic Monitoring and for Autonomous and Connected Vehicles

by Sergio Saponara, Stefano Giordano and Riccardo Mariani

Sensors 2021, 21(5), 1648; https://doi.org/10.3390/s21051648 - 27 Feb 2021

Cited by 2 | Viewed by 3281

Abstract

This Editorial analyzes the manuscripts accepted, after a careful peer-reviewed process, for the special issue “IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles” of the Sensors MDPI journal.[...] Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

Research

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23 pages, 6863 KiB

Open AccessArticle

Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

by Maria V. Peppa, Tom Komar, Wen Xiao, Phil James, Craig Robson, Jin Xing and Stuart Barr

Sensors 2021, 21(2), 629; https://doi.org/10.3390/s21020629 - 18 Jan 2021

Cited by 16 | Viewed by 4409

Abstract

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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14 pages, 454 KiB

Open AccessArticle

Performance Evaluation of Attribute-Based Encryption in Automotive Embedded Platform for Secure Software Over-The-Air Update

by Michele La Manna, Luigi Treccozzi, Pericle Perazzo, Sergio Saponara and Gianluca Dini

Sensors 2021, 21(2), 515; https://doi.org/10.3390/s21020515 - 13 Jan 2021

Cited by 34 | Viewed by 5156

Abstract

This paper aims to show that it is possible to improve security for over the air update functionalities in an automotive scenario through the use of a cryptographic scheme, called “Attribute-Based-Encryption” (ABE), which grants confidentiality to the software/firmware update done Over The Air (OTA). We demonstrate that ABE is seamlessly integrable into the state of the art solutions regarding the OTA update by showing that the overhead of the ABE integration in terms of computation time and its storage is negligible w.r.t. the other overheads that are introduced by the OTA process, also proving that security can be enhanced with a minimum cost. In order to support our claim, we report the experimental results of an implementation of the proposed ABE OTA technique on a Xilinx ZCU102 evaluation board, which is an automotive-oriented HW/SW platform that is equipped with a Zynq UltraScale+ MPSoC chip that is representative of the computing capability of real automotive Electronic Control Units (ECUs). Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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18 pages, 3140 KiB

Open AccessArticle

An Indoor Robust Localization Algorithm Based on Data Association Technique

by Long Cheng, Yong Wang, Mingkun Xue and Yangyang Bi

Sensors 2020, 20(22), 6598; https://doi.org/10.3390/s20226598 - 18 Nov 2020

Cited by 9 | Viewed by 2186

Abstract

As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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18 pages, 15307 KiB

Open AccessArticle

Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model

by Delia-Georgiana Stuparu, Radu-Ioan Ciobanu and Ciprian Dobre

Sensors 2020, 20(22), 6485; https://doi.org/10.3390/s20226485 - 13 Nov 2020

Cited by 19 | Viewed by 6325

Abstract

In order to improve the traffic in large cities and to avoid congestion, advanced methods of detecting and predicting vehicle behaviour are needed. Such methods require complex information regarding the number of vehicles on the roads, their positions, directions, etc. One way to obtain this information is by analyzing overhead images collected by satellites or drones, and extracting information from them through intelligent machine learning models. Thus, in this paper we propose and present a one-stage object detection model for finding vehicles in satellite images using the RetinaNet architecture and the Cars Overhead With Context dataset. By analyzing the results obtained by the proposed model, we show that it has a very good vehicle detection accuracy and a very low detection time, which shows that it can be employed to successfully extract data from real-time satellite or drone data. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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28 pages, 7930 KiB

Open AccessArticle

Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)

by Joy Eze, Sijing Zhang, Enjie Liu and Elias Eze

Sensors 2020, 20(21), 6402; https://doi.org/10.3390/s20216402 - 9 Nov 2020

Cited by 12 | Viewed by 2873

Abstract

Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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13 pages, 6935 KiB

Open AccessArticle

A Deep Learning Approach for Estimating Traffic Density Using Data Obtained from Connected and Autonomous Probes

by Daisik Nam, Riju Lavanya, R. Jayakrishnan, Inchul Yang and Woo Hoon Jeon

Sensors 2020, 20(17), 4824; https://doi.org/10.3390/s20174824 - 26 Aug 2020

Cited by 12 | Viewed by 4527

Abstract

The focus of this research is on the estimation of traffic density from data obtained from Connected and Autonomous Probes (CAPs). CAPs pose an advantage over expensive and invasive infrastructure such as loop detectors. CAPs maneuver their driving trajectories, sensing the presence of adjacent vehicles and distances to them by means of several electronic sensors, whose data can be used for more sophisticated traffic density estimation techniques. Traffic density has a highly nonlinear nature during on-congestion and queue-clearing conditions. Closed-mathematical forms of the traditional density estimation techniques are incapable of dealing with complex nonlinearities, which opens the door for data-driven approaches such as machine learning techniques. Deep learning algorithms excel in data-rich contexts, which recognize nonlinear and highly situation-dependent patterns. Our research is based on an LSTM (Long short-term memory) neural network for the nonlinearity associated with time dynamics of traffic flow. The proposed method is designed to learn the input-output relation of Edie’s definition. At the same time, the method recognizes a temporally nonlinear pattern of traffic. We evaluate our algorithm by using a microscopic simulation program (PARAMICS) and demonstrate that our model accurately estimates traffic density in Free-flow, Transition, and Congested conditions. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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27 pages, 1837 KiB

Open AccessArticle

Sensor-Aided V2X Beam Tracking for Connected Automated Driving: Distributed Architecture and Processing Algorithms

by Mattia Brambilla, Lorenzo Combi, Andrea Matera, Dario Tagliaferri, Monica Nicoli and Umberto Spagnolini

Sensors 2020, 20(12), 3573; https://doi.org/10.3390/s20123573 - 24 Jun 2020

Cited by 23 | Viewed by 5870

Abstract

This paper focuses on ultra-reliable low-latency Vehicle-to-Anything (V2X) communications able to meet the extreme requirements of high Levels of Automation (LoA) use cases. We introduce a system architecture and processing algorithms for the alignment of highly collimated V2X beams based either on millimeter-Wave (mmW) or Free-Space Optics (FSO). Beam-based V2X communications mainly suffer from blockage and pointing misalignment issues. This work focuses on the latter case, which is addressed by proposing a V2X architecture that enables a sensor-aided beam-tracking strategy to counteract the detrimental effect of vibrations and tilting dynamics. A parallel low-rate, low-latency, and reliable control link, in fact, is used to exchange data on vehicle kinematics (i.e., position and orientation) that assists the beam-pointing along the line-of-sight between V2X transceivers (i.e., the dominant multipath component for mmW, or the direct link for FSO). This link can be based on sub-6 GHz V2X communication, as in 5G frequency range 1 (FR1). Performance assessments are carried out to validate the robustness of the proposed methodology in coping with misalignment induced by vehicle dynamics. Numerical results show that highly directional mmW and/or FSO communications are promising candidates for massive data-rate vehicular communications even in high mobility scenarios. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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16 pages, 7684 KiB

Open AccessArticle

Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication

by Maciej Nikodem, Mariusz Słabicki, Tomasz Surmacz, Paweł Mrówka and Cezary Dołęga

Sensors 2020, 20(11), 3334; https://doi.org/10.3390/s20113334 - 11 Jun 2020

Cited by 26 | Viewed by 7013

Abstract

Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. However, this requires large volumes of data to be transmitted and may raise privacy issues. This paper presents a dedicated deep learning detection and tracking algorithms that can be run directly on the camera’s embedded system. This method significantly reduces the stream of data from the cameras, reduces the required communication bandwidth and expands the range of communication technologies to use. Consequently, it allows to use short-range radio communication to transmit vehicle-related information directly between the cameras, and implement the multi-camera tracking directly in the cameras. The proposed solution includes detection and tracking algorithms, and a dedicated low-power short-range communication for multi-target multi-camera tracking systems that can be applied in parking and intersection scenarios. System components were evaluated in various scenarios including different environmental and weather conditions. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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Figure 1
High level architecture of the vehicle tracking system. Black and red arrows denote local and global communication respectively. Full article ">Figure 2
General structure of the neural network used for vehicle detection. Full article ">Figure 3
Example of detection input (A), and the output: heatmap (B), bounding box offsets (C), detection at scale 0 (D) and scale 1 (E). Full article ">Figure 4
Deployment of the test system at university campus—camera locations and camera #1 FoV. Full article ">Figure 5
Packet reception rate (PRR) during normal operation (a) and PRR/DRR values under heavy traffic (b). Full article ">Figure 6
Example of single camera tracking. Images 1–11 present subsequent snapshots of the recording with two vehicles tracked. The first vehicle is visible on frames 1–4, the second one on all the snapshots. On snapshot 9 the vehicle is incorrectly identified as a new one. Full article ">Figure 7
Possible trajectories of vehicles (trajectory 6 is not tracked) and examples of tracking for trajectories 4 and 5. Blue line marks the region of interest—objects outside are excluded from tracking. Red line is a line of detection—vehicles crossing this line are automatically assigned to trajectory 1 or 3, depending on the direction they move. For clarity, each of the vehicle tracked is presented on a separate snapshot. Full article ">Figure 8
Simple multi-camera tracking system for a parking. The cameras track the vehicles from the barrier to the parking space. Full article ">

21 pages, 4814 KiB

Open AccessArticle

PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction

by Petros Karvelis, Daniele Mazzei, Matteo Biviano and Chrysostomos Stylios

Sensors 2020, 20(11), 3181; https://doi.org/10.3390/s20113181 - 3 Jun 2020

Cited by 5 | Viewed by 4578

Abstract

Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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14 pages, 1829 KiB

Open AccessArticle

Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles

by Miloš Viktorović, Dujuan Yang and Bauke de Vries

Sensors 2020, 20(10), 2961; https://doi.org/10.3390/s20102961 - 23 May 2020

Cited by 16 | Viewed by 4318

Abstract

For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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15 pages, 12267 KiB

Open AccessArticle

Research and Implementation of Vehicle Target Detection and Information Recognition Technology Based on NI myRIO

by Hongliang Wang, Shuang He, Jiashan Yu, Luyao Wang and Tao Liu

Sensors 2020, 20(6), 1765; https://doi.org/10.3390/s20061765 - 22 Mar 2020

Cited by 11 | Viewed by 4851

Abstract

A vehicle target detection and information extraction scheme based on NI (National Instruments) myRIO is designed in this paper. The vehicle information acquisition and processing method based on image recognition is used to design a complete vehicle detection and information extraction system. In the LabVIEW programming environment, the edge detection method is used to realize the vehicle target detection, the pattern matching method is used to realize the vehicle logo recognition, and the Optical Character Recognition (OCR) character recognition algorithm is used to realize the vehicle license plate recognition. The feasibility of the design scheme in this paper is verified through the actual test and analysis. The scheme is intuitive and efficient, with the high recognition accuracy. Full article

(This article belongs to the Special Issue IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles)

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IoT Sensing Systems for Traffic Monitoring and for Automated and Connected Vehicles

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