Advances in Vehicle Technology and Intelligent Transport Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 9088

Special Issue Editor

Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague, Czech Republic
Interests: combinatorial optimization; Petri nets; scheduling algorithms; real time; autonomous cars

Special Issue Information

Dear Colleagues,

There has been an increasing level of demand for faster, safer, and greener transportation systems with higher levels of capacity and convenience, in the face of a new era of intelligent transportation systems (ITS) empowered by artificial intelligence (AI) technologies. ITS include telematics and all types of communications in vehicles, between vehicles (e.g., V2V), and between vehicles and infrastructure (e.g., V2I).

Therefore, the purpose of this Special Issue is to publish high-quality research, with expected submissions from both from academic and industrial stakeholders, which will serve as an outlet for disseminating innovative solutions towards meeting the expectation of ITS. Original, high-quality contributions that have not yet been published, submitted, or are not currently under review by other journals or peer-reviewed conferences are sought.

Subjects of interest include, but are not limited to, the following topics:

  • Artificial and computational intelligence;
  • Autonomous driving and autonomous vehicles;
  • Traffic and flow optimization techniques;
  • Real-time and dynamic prediction of traffic flows;
  • Advanced driver assistance systems;
  • Novel architectures for ITS.

Dr. Zdenek Hanzalek
Guest Editor

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. Applied Sciences 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 2400 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

  • electric/autonomous vehicles
  • reliability and security in transport
  • traffic and flow optimization techniques
  • applications of autonomous vehicles

Published Papers (5 papers)

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Research

15 pages, 4965 KiB  
Article
Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System
by Gian Carlo Angles Medina, Karina Rosas Paredes, Manuel Zúñiga Carnero and José Sulla-Torres
Appl. Sci. 2022, 12(19), 9955; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199955 - 03 Oct 2022
Viewed by 1349
Abstract
Currently, there are tire retreading companies whose evaluations are not wholly accurate; due to various factors, sometimes customers are forced to agree to what is decided, and this means that the customer can sometimes pay for services that do not necessarily guarantee the [...] Read more.
Currently, there are tire retreading companies whose evaluations are not wholly accurate; due to various factors, sometimes customers are forced to agree to what is decided, and this means that the customer can sometimes pay for services that do not necessarily guarantee the correct operation of the tire or, failing that, shorten its life. This work aims to develop a tire diagnostic system that allows for evaluating a tire’s faults and can thus be more precise when determining if it needs retreading or a change process. The diagnostic system is focused on demonstrating that fuzzy logic can be applied in diagnosing the condition of tires. The methodology consisted of determining the variables to be considered in the evaluation of tires, such as blowing out, flange breakage, band failure, and patching, then applying fuzzy logic. Subsequently, the execution tests of the built diagnostic software were carried out for its validation in a case study of a tire retreading company. The result was a margin of error of 1.6% accuracy versus 5.6% from the operator experience. The conclusion was that fuzzy logic could be applied correctly in the field of tire retreading, providing substantial savings in time and resources for related companies, as well as giving customers confidence since, by using more accurate results, the diagnostic system will make the tire evaluation efficient. Full article
(This article belongs to the Special Issue Advances in Vehicle Technology and Intelligent Transport Systems)
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14 pages, 2367 KiB  
Article
Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
by Yaping Tian, Chao Yang, Junjie Yang and Xinming Nie
Appl. Sci. 2022, 12(12), 6152; https://0-doi-org.brum.beds.ac.uk/10.3390/app12126152 - 17 Jun 2022
Cited by 2 | Viewed by 1340
Abstract
Efficient data sharing schemes are one of the key technologies in the Internet of Vehicles (IoV). However, the insufficient willingness of vehicle users to provide data makes the traditional blockchain-based IoV network have low throughput. The income of IoV providers decreases when the [...] Read more.
Efficient data sharing schemes are one of the key technologies in the Internet of Vehicles (IoV). However, the insufficient willingness of vehicle users to provide data makes the traditional blockchain-based IoV network have low throughput. The income of IoV providers decreases when the vehicle density increases on the road. In this paper, we investigated a mobile vehicle data sharing scheme based on the consortium blockchain. In detail, the consortium blockchain was used to limit the degree of decentralization and openness, and the optimal revenue strategy approach between vehicles and data-demand devices was obtained through the Stackelberg game. The load test library based on Node.js was used to simulate and compare the data transmission performance of the proposed consortium blockchain with traditional blockchain schemes. Simulation results show that the proposed scheme had higher buyer’s revenue, and the block transmission performance was significantly higher than that of traditional blockchain schemes. Full article
(This article belongs to the Special Issue Advances in Vehicle Technology and Intelligent Transport Systems)
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14 pages, 5234 KiB  
Article
EnGS-DGR: Traffic Flow Forecasting with Indefinite Forecasting Interval by Ensemble GCN, Seq2Seq, and Dynamic Graph Reconfiguration
by Shi-Yuan Han, Qiang Zhao, Qi-Wei Sun, Jin Zhou and Yue-Hui Chen
Appl. Sci. 2022, 12(6), 2890; https://0-doi-org.brum.beds.ac.uk/10.3390/app12062890 - 11 Mar 2022
Cited by 3 | Viewed by 1816
Abstract
An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network [...] Read more.
An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California’s Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics. Full article
(This article belongs to the Special Issue Advances in Vehicle Technology and Intelligent Transport Systems)
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17 pages, 7299 KiB  
Article
A Study on Highway Driving Assist Evaluation Method Using the Theoretical Formula and Dual Cameras
by Bong-Ju Kim and Seon-Bong Lee
Appl. Sci. 2021, 11(24), 11903; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411903 - 14 Dec 2021
Viewed by 1455
Abstract
In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems using the theoretical formula and dual cameras, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required in such tests. A theoretical [...] Read more.
In this paper, we propose a method to evaluate Highway Driving Assist (HDA) systems using the theoretical formula and dual cameras, which eliminates the need of experts or expensive equipment and reduces the time, effort, and cost required in such tests. A theoretical evaluation formula that can be calculated was proposed and used. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, HDA safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum errors were within 10%. It was determined that the representative cause of the maximum error occurred in the dual camera installed in the test vehicle. Problems such as road surface vibration, shaking due to air resistance, changes in ambient brightness, and the process of focusing the video occurred during driving. In the future, it is judged that it will be necessary to verify the complex transportation environment during morning and evening rush hour, and it is believed that tests will be needed in bad weather such as snow and rain. Full article
(This article belongs to the Special Issue Advances in Vehicle Technology and Intelligent Transport Systems)
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18 pages, 5945 KiB  
Article
A Simplified Vehicle Dynamics Model for Motion Planner Designed by Nonlinear Model Predictive Control
by Feng Gao, Qiuxia Hu, Jie Ma and Xiangyu Han
Appl. Sci. 2021, 11(21), 9887; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219887 - 22 Oct 2021
Cited by 4 | Viewed by 2414
Abstract
Motion planning by considering it as an optimal problem is an effective and widely applicable method. Its comprehensive performance greatly depends on the vehicle dynamics model, which is highly coupled and nonlinear, especially under the dynamical scenarios and causes much more consumption of [...] Read more.
Motion planning by considering it as an optimal problem is an effective and widely applicable method. Its comprehensive performance greatly depends on the vehicle dynamics model, which is highly coupled and nonlinear, especially under the dynamical scenarios and causes much more consumption of computation resources for the numerical optimization. To increase the real time performance of the motion planner designed by nonlinear model predictive control (NMPC), a unified and simplified vehicle dynamics model (SDM) is presented to make a balance between the accuracy and complexity for dynamical driving scenarios. Based on the statistical analysis results of naturalistic driving conditions, a unified nonlinear vehicle dynamics model is set up, which considers the tyre cornering characteristic and is also applicable to conditions with large turning angle. After the validation of this coupled dynamics model (CDM) by comparisons with other widely used models under a variety of conditions, the coupling effect is analyzed according to the transfer functions, which are obtained by linearizing CDM at equilibrium points. Furthermore, SDM is derived by ignoring the weak part of the coupling effect. The accuracy of SDM is validated by several comparative studies with other models and it is further applied to design a motion planner by NMPC to validate its contribution on the performance improvement under dynamical driving conditions. Full article
(This article belongs to the Special Issue Advances in Vehicle Technology and Intelligent Transport Systems)
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