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Electronics
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Sensor Technologies for Intelligent Transportation Systems
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Sensor Technologies for Intelligent Transportation Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1793

Special Issue Editors

Dr. Xiwei Bai

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: visual-inertial navigation system; visual-aided GNSS positioning; sensor fusion; autonomous driving
Dr. Yiran Luo

E-Mail Website
Guest Editor
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Interests: GNSS accelerometer; low-power GNSS ICs/modules; innovative GNSS technology

Special Issue Information

Dear Colleagues,

Sensor technologies are becoming increasingly pivotal in the evolution of Intelligent Transportation Systems (ITSs), which are at the forefront of the smart mobility revolution. As we advance into an era in which transportation is not just about mobility but also about connectivity and real-time data exchange, sensors act as the critical nerve endings of ITSs, providing the essential data required for intelligent decision making. The integration of sophisticated sensor networks is vital for enhancing traffic management, improving road safety, reducing environmental impacts, and facilitating autonomous vehicle technologies. This Special Issue is dedicated to showcasing cutting-edge research and developments in sensor technologies that can be integrated into ITSs to address these challenges. Contributions may include studies on the use of various types of sensors such as LiDAR, radar, ultrasonic, camera, and thermal sensors, as well as the role of the Internet of Things (IoT) in enhancing sensor capabilities within ITS. Through this collection of high-quality research articles, we hope to foster the development of smarter, safer, and more efficient transportation systems for the future.

Topics of interests include, but are not limited to, the following:

  1. Multi-sensor fusion for intelligent transportation system localization, including GNSS, IMU, LiDAR, camera, and high-definition map.
  2. The integration of sensor data with vehicular and infrastructural systems.
  3. Sensor-based predictive maintenance for transportation infrastructure
  4. The application of machine learning and artificial intelligence in processing sensor data
  5. The reliability and accuracy of sensor data study

Dr. Xiwei Bai
Dr. Yiran Luo
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. Electronics 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

  • multi-sensor fusion
  • autonomous vehicle positioning
  • AI
  • road safety
  • sensor data prosessing

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Published Papers (2 papers)

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27 pages, 11986 KiB  
Article
Robust Regression and Redundant Measurement Noise Estimation Adaptive Filtering for Localization in Urban Environments
by Li Zha, Hai Zhang, Aiping Wang and Cancan Tao
Electronics 2025, 14(5), 826; https://doi.org/10.3390/electronics14050826 - 20 Feb 2025
Viewed by 388
Abstract
This paper focuses on a solution of target self-positioning when a Global Navigation Satellite System (GNSS) is denied. It is composed of Inertial Navigation Systems (INS), Signals of Opportunities (SOPs), and a navigation prototype. One of the options for navigation via SOP (NAVSOP) [...] Read more.
This paper focuses on a solution of target self-positioning when a Global Navigation Satellite System (GNSS) is denied. It is composed of Inertial Navigation Systems (INS), Signals of Opportunities (SOPs), and a navigation prototype. One of the options for navigation via SOP (NAVSOP) is to utilize cellular signals, such as Long Time Evolution (LTE). When the prior information is insufficient, the location of the base station (BS) is obtained by collecting the demodulation of the downlink signal, and the synchronization signal is used for static time offset correction. In view of the large positioning error of the trilateral positioning method based on Received Signal Strength (RSS), a multi-station positioning optimization method is proposed by introducing the robust regression. Monte Carlo simulation experiments indicate that the method has improved the positioning failure and insufficient accuracy. Aiming at the influence of the state estimation errors on filtering results, the Second Order Mutual Difference (SOMD) method with the noise covariance R, which is independent of the existing Extended Kalman Filter (EKF) framework and combined with Redundant Measurement Noise Covariance Estimation (RMNCE), is applied to the model. The simulation results show that the average error of the robust model is 10.28 m, which is better than the EKF method. Finally, a vehicle test in constant speed has been carried out. The results show that the proposed model can realize self-positioning with limited BS location information, and the positioning accuracy can reach 11.68 m over a 283 m trajectory. Full article
(This article belongs to the Special Issue Sensor Technologies for Intelligent Transportation Systems)
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11 pages, 2256 KiB  
Article
Accessible and Inexpensive Parameter Testing Platform for Adhesive Removal in Mechanical Exfoliation Procedures
by Anthony Gasbarro, Yong-Sung D. Masuda, Richard C. Ordonez, Jeffrey A. Weldon and Victor M. Lubecke
Electronics 2025, 14(3), 533; https://doi.org/10.3390/electronics14030533 - 28 Jan 2025
Viewed by 954
Abstract
Mechanical exfoliation of two-dimensional (2D) materials using adhesive tape is a widely used method for producing high-quality single-layer graphene flakes. However, this technique is time-consuming, with low yields and inconsistent results due to process variations and human error. This paper introduces a modular [...] Read more.
Mechanical exfoliation of two-dimensional (2D) materials using adhesive tape is a widely used method for producing high-quality single-layer graphene flakes. However, this technique is time-consuming, with low yields and inconsistent results due to process variations and human error. This paper introduces a modular system designed to rigorously test and optimize the conditions for 2D material deposition, with a focus on graphene. The system is adaptable to a range of inexpensive, commercially available linear stages and stepper motors, providing precise, independent control over key parameters such as peel speed and angle—both of which are critical in deposition yields. Tests confirmed the system’s accuracy within ±0.7% relative speed error across a range of speeds (1 μm/s to 5000 μm/s) and peel angle control from 0∘ to 120∘. Additionally, the system automates control of the key factors at the most demanding step of the exfoliation process while being affordable and easily assembled, making it accessible for laboratories and educational institutions to explore the optimal conditions for scaling 2D material production. This system offers the capability to gain critical insights into the exfoliation process, driving improved yields and scalability, which are essential for fabricating highly specialized devices that rely on 2D materials. Full article
(This article belongs to the Special Issue Sensor Technologies for Intelligent Transportation Systems)
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