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Feature Papers in Vehicular Sensing 2023

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3644

Special Issue Editors


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Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: intelligent transport systems; advanced driver assistance systems; vehicle positioning; inertial sensors; digital maps; vehicle dynamics; driver monitoring; perception; autonomous vehicles; cooperative services; connected and autonomous driving
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008-5329, USA
Interests: signal processing; machine learning; artificial intelligence; data fusion; autonomous vehicles; localization and object tracking

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Guest Editor
Department of Computing and Systems Engineering, University of Zaragoza, Teruel, Spain
Interests: vehicular networks; intelligent transportation system; information technologies; electric vehicle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Feature Special Issue for Vehicular Sensing, namely the Special Issue, “Feature Papers in Vehicular Sensing”.

We are pleased to announce that the Section Vehicular Sensing is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our Section and outstanding scholars in this research field. We welcome contributions, as well as recommendations, from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typify the very best insightful and influential original articles or reviews in which our Section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted. 

We would also like to take this opportunity to call on more scholars to join the Section Vehicular Sensing, so that we can work together to further develop this exciting field of research. Potential topics include, but are not limited to, the following:

  • Unmanned aerial vehicle (UAV);
  • Unmanned aircraft;
  • Underwater vehicles;
  • Drones;
  • Cyber ship;
  • Intelligent transportation systems;
  • Intelligent vehicles;
  • Traffic monitoring;
  • Vehicle detection;
  • Vehicle localization system;
  • Smart mobility and sustainable transport services;
  • Driver behavior monitoring;
  • Sensors for fault detection of vehicles;
  • Fault-tolerant systems;
  • Cyber security in vehicle systems and networks;
  • Connected and autonomous vehicles;
  • Vehicular social networks (VSNs);
  • Connected vehicles on urban roads;
  • Internet of vehicles;
  • Vehicular networks;
  • Vehicle-to-everything;
  • Blockchain in vehicles;
  • Wireless in-car networks;
  • Underwater communications;
  • Vehicular ad hoc networks (VANETs);
  • Vehicle communications (V2X, V2V; V2I);
  • Emerging IoT applications in vehicular social networks (VSNs);
  • Vehicle privacy and trust;
  • Artificial intelligence in automated vehicles, e.g., self-driving cars;
  • Big data analysis in vehicular systems and networks;
  • Cyberphysical system control and safety in vehicular networks.

Dr. Felipe Jiménez
Prof. Dr. Ikhlas Abdel-Qader
Prof. Dr. Piedad Garrido Picazo
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 (4 papers)

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Research

15 pages, 6853 KiB  
Article
Analysis of the Scenarios of Use of an Innovative Technology for the Fast and Nondestructive Characterization of Viscoelastic Materials in the Tires Field
by Flavio Farroni, Francesco Timpone and Andrea Genovese
Sensors 2024, 24(4), 1136; https://doi.org/10.3390/s24041136 - 09 Feb 2024
Viewed by 457
Abstract
The properties of tires related to their viscoelastic behavior have a significant impact in the field of vehicle dynamics. They affect the performance and safety of a vehicle based on how they change when the tire performs in variable thermal conditions, interacts with [...] Read more.
The properties of tires related to their viscoelastic behavior have a significant impact in the field of vehicle dynamics. They affect the performance and safety of a vehicle based on how they change when the tire performs in variable thermal conditions, interacts with various kinds of road surfaces, and accumulates mileage over time. To analyze and understand such properties of viscoelastic materials, destructive tests like dynamic mechanical analysis (DMA) are used, which make the tire unusable after the test; these are usually carried out on specimens cut from the zone of interest. The development of an innovative testing methodology connected to a hardware device called VESevo allows the characterization of the viscoelastic properties of tire compounds belonging to tread or other parts in a fast and nondestructive way. This new device provides valuable information about the evolution of the tire’s viscoelastic properties, allowing it to monitor them throughout the whole lifecycle. In the paper, an overview of the possible sensitivities that can be investigated thanks to the VESevo is provided: The tread viscoelasticity was characterized and monitored for several tire tread compounds, over tire mileage, over tread thermal curing cycles, and as an index of the tread quality and uniformity in production. Preliminary results were collected and are presented. In the final paragraph, further recent applications developed from the tire field, which are not directly related, are reported. Full article
(This article belongs to the Special Issue Feature Papers in Vehicular Sensing 2023)
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18 pages, 2120 KiB  
Article
Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case
by Alessio Capello, Matteo Fresta, Francesco Bellotti, Hamed Haghighi, Johannes Hiller, Sajjad Mozaffari and Riccardo Berta
Sensors 2023, 23(18), 7866; https://doi.org/10.3390/s23187866 - 13 Sep 2023
Cited by 1 | Viewed by 622
Abstract
As timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to [...] Read more.
As timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project’s progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project’s state, 330+ numerical indicators were identified. According to the project’s pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors—real or virtual—deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context. Full article
(This article belongs to the Special Issue Feature Papers in Vehicular Sensing 2023)
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17 pages, 5846 KiB  
Article
Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks
by Feng Gao, Fengkui Zhao and Yong Zhang
Sensors 2023, 23(16), 7222; https://doi.org/10.3390/s23167222 - 17 Aug 2023
Cited by 3 | Viewed by 1002
Abstract
With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy [...] Read more.
With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle’s stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article. Full article
(This article belongs to the Special Issue Feature Papers in Vehicular Sensing 2023)
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26 pages, 3971 KiB  
Article
A Mission-Oriented Flight Path and Charging Mechanism for Internet of Drones
by Chenn-Jung Huang, Kai-Wen Hu, Hao-Wen Cheng and Yi-Sin Sie Lin
Sensors 2023, 23(9), 4269; https://doi.org/10.3390/s23094269 - 25 Apr 2023
Viewed by 1039
Abstract
In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the [...] Read more.
In addition to traditional battery exchange services and stationary charging stations, researchers have proposed wireless charging technology, such as decentralized laser charging or drone-to-drone charging in flight, to provide power to drones with insufficient battery electricity. However, the charging methods presented in the literature will inevitably cause drones to wait in line for charging during peak hours and disrupt their scheduled trips when the number of drones grows rapidly in the future. To the best of our knowledge, there have been no integrated solutions for drone flight path and charging planning to alleviate charging congestion, taking into account the different mission characteristics of drones and the charging cost considerations of drone operators. Accordingly, this paper provides adaptive charging options to help drone operators to solve the above-mentioned problems. Drones on ordinary missions can use conventional battery swap services, wired charging stations, or electromagnetic wireless charging stations to recharge their batteries as usual, whereas drones on time-critical missions can choose drone-to-drone wireless charging or decentralized laser charging deployed along the fight paths to charge the batteries of drones in flight. Notably, since fixed-wing drones have larger wing areas to install solar panels, they can also use solar energy to charge during flight if the weather is clear. The simulation results exhibited that the proposed work reduced the power load of the power grid during peak hours, met the charging needs of each individual drone during flight, and cut down the charging costs of drone operators. As a result, an all-win situation for drone operators, drone customers, and power grid operators was achieved. Full article
(This article belongs to the Special Issue Feature Papers in Vehicular Sensing 2023)
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