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Integration of Sensor Technologies and Artificial Intelligence Strategies for Autonomous Vehicles and Intelligent Transportation Systems

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 655

Special Issue Editor

Dr. Tomás Mateo Sanguino

E-Mail Website
Guest Editor
Department of Electronic Engineering, Computer Systems and Automatics, University of Huelva, Av. de las Artes s/n, 21007 Huelva, Spain
Interests: road safety; communications; cybersecurity; smart city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of sensor technologies with artificial intelligence (AI) strategies is revolutionizing autonomous vehicles (AVs) and intelligent transportation systems (ITSs). This Special Issue explores the latest advances in AI techniques, such as machine learning and embedded computer vision, to optimize performance in environments with resource-constrained hardware and limited computing capacity. These innovations aim to enhance safety, efficiency, and decision making in real-time scenarios, paving the way for smarter, more autonomous mobility solutions.

The topic of this Special Issue aligns with the scope of the journal Sensors (MDPI) by focusing on advanced sensor systems and their integration with AI for real-time data processing and decision making. It addresses key themes of the journal, such as sensor fusion, machine learning, and embedded systems, emphasizing efficient, real-world applications in autonomous vehicles and transportation networks, particularly in resource-constrained environments. This makes it highly relevant to Sensors' focus on innovative sensor technologies and their practical uses.

Dr. Tomás Mateo Sanguino
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. 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

  • autonomous vehicles
  • intelligent transportation systems
  • sensor integration
  • embedded computer vision
  • artificial intelligence
  • real-time decision making
  • resource-constrained hardware
  • sensor fusion
  • edge computing
  • cloud computing

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Published Papers (1 paper)

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Research

18 pages, 7715 KiB  
Article
Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR)
by Meiting Jin and Junxing Zhang
Sensors 2024, 24(21), 6987; https://doi.org/10.3390/s24216987 - 30 Oct 2024
Viewed by 497
Abstract
Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 [...] Read more.
Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection. Full article
(This article belongs to the Special Issue Integration of Sensor Technologies and Artificial Intelligence Strategies for Autonomous Vehicles and Intelligent Transportation Systems)
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