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Intelligent Transportation Systems: Sensing, Automation and Control

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1189

Special Issue Editors

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: computational experiments; urban rail transit; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
Interests: computer vision (unmanned vehicle); ship trajectory data mining; maritime intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In intelligent transportation systems (ITS), sensing is an essential component for data acquisition and information processing, creating a bridge that connects cyber, physical, and transportation space. It plays a critical role in designing safe, efficient, and ecologically responsible ITS. The complexity and dynamics of traffic environments require advanced sensing capabilities, as well as effective automation and control methods based on these sensors. The advancement in cutting-edge technologies, such as AIGC, edge computing, federal computing, V2X communication, graph reinforcement learning, and others related to transportation systems’ sensing, automation, and control, is essential for ITS development. Therefore, developing new concepts, methodologies, tools, algorithms, and applications for future ITS based on emerging technologies is of the utmost importance and has great potential.

The primary objective of this Special Issue is to provide an opportunity to share experiences, discuss solutions, and exchange ideas among the industry, academia, and the public sector. We welcome original papers that address various aspects of ITS, including, but not limited to, the following:

  • Multi-model data fusion for ITS;
  • ChatGPT-powered Sensing for ITS;
  • Virtual sensing and data generation;
  • 2D and 3D sensing;
  • Cognitive sensing;
  • Parallel sensing;
  • Crypto sensing;
  • Federated sensing;
  • Social sensing;
  • Ecological sensing;
  • Large-scale traffic data processing;
  • Traffic knowledge graph for ITS operations;
  • Crowdsourcing for sensing, managing, and controlling of ITS;
  • City-scale traffic simulation with high-performance computing techniques;
  • Big data for ITS modeling;
  • Travel behavior analysis;
  • Pattern mining for individual mobility and activity behavior;
  • Energy consumption, efficiency and environment research for ITS;
  • Graph reinforcement learning for ITS;
  • Ride-sharing for urban mobility;
  • Case studies.

Dr. Fenghua Zhu
Dr. Ryan Wen Liu
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

  • intelligent transportation systems
  • multimodal data fusion
  • intelligent sensing
  • scenarios engineering
  • artificial intelligence
  • big data
  • connected and autonomous vehicles
  • knowledge graph
  • graph deep learning
  • computer vision

Published Papers (2 papers)

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Research

18 pages, 5137 KiB  
Article
Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network
Sensors 2024, 24(4), 1280; https://doi.org/10.3390/s24041280 - 17 Feb 2024
Viewed by 219
Abstract
Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based [...] Read more.
Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Sensing, Automation and Control)
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19 pages, 8065 KiB  
Article
A Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value
Sensors 2023, 23(21), 8670; https://doi.org/10.3390/s23218670 - 24 Oct 2023
Viewed by 487
Abstract
Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is [...] Read more.
Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Sensing, Automation and Control)
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