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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 3923

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 (4 papers)

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Research

21 pages, 4210 KiB  
Article
Cross-Field Road Markings Detection Based on Inverse Perspective Mapping
by Eric Hsueh-Chan Lu and Yi-Chun Hsieh
Sensors 2024, 24(24), 8080; https://doi.org/10.3390/s24248080 - 18 Dec 2024
Viewed by 595
Abstract
With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road [...] Read more.
With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road markings data and label them by ourselves manually, which is huge labor work and takes lots of time. Moreover, object detection often encounters the problem of small object detection. The detection accuracy often decreases when the detection distance increases. This is primarily because distant objects on the road take up few pixels in the image and object scales vary depending on different distances and perspectives. For the sake of solving the issues mentioned above, this paper utilizes a virtual dataset and open dataset to train the object detection model and cross-field testing in the field of Taiwan roads. In order to make the model more robust and stable, the data augmentation method is employed to generate more data. Therefore, the data are increased through the data augmentation method and homography transformation of images in the limited dataset. Additionally, Inverse Perspective Mapping is performed on the input images to transform them into the bird’s eye view, which solves the “small objects at far distance” problem and the “perspective distortion of objects” problem so that the model can clearly recognize the objects on the road. The model testing on the front-view images and bird’s eye view images also shows a remarkable improvement of accuracy by 18.62%. 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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30 pages, 14923 KiB  
Article
Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles
by Bohua Sun, Yingjie Shan, Guanpu Wu, Shuai Zhao and Fei Xie
Sensors 2024, 24(24), 7904; https://doi.org/10.3390/s24247904 - 11 Dec 2024
Cited by 1 | Viewed by 1130
Abstract
The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, [...] Read more.
The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems. 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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16 pages, 9530 KiB  
Article
Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations
by Donghyun Kim and Yonghwan Jeong
Sensors 2024, 24(23), 7802; https://doi.org/10.3390/s24237802 - 5 Dec 2024
Viewed by 761
Abstract
This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The [...] Read more.
This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird’s-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions. 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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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
Cited by 3 | Viewed by 980
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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