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Intelligent Vehicle, Infrastructure Perception and Control Based on Imaging and Sensing

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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 5757

Special Issue Editors

Dr. Lei Zhu

E-Mail Website
Guest Editor
Department of Systems Engineering and Engineering Management, University of North Carolina at Charlotte, Charlotte, NC, USA
Interests: smart and sustainable mobility systems; spatial sensing technologies; human mobility modeling and Wi-Fi data processing; emerging mobility modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Xu

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, University of Nevada, Reno, NV, USA
Interests: collection and analysis of roadside LiDAR data; vehicle operation cost evaluation; intelligent transportation systems including connected vehicles; data-driven traffic safety analysis
Dr. Stanley Young

E-Mail Website
Guest Editor
Center for Integrated Mobility Sciences, National Renewable Energy Laboratory, Golden, CO, USA
Interests: advanced mobility systems and sustainability; automated electric shuttles; mobility energy productivity metric; infrastructure perception and control; computer vision

Special Issue Information

Dear Colleagues,

In the last few years, we have seen a growing interest in intelligent vehicles and smart transportation, which are improving many aspects of transportation systems, such as road safety, mobility efficiency, signal control optimization, and energy efficiency. With sensors, including cameras, LiDAR, radar, advanced imaging, 3D point cloud, and communication technologies, the automotive industry and transportation systems are moving toward intelligent vehicles and advanced infrastructure perception and. Intelligent vehicles and roadway infrastructure need to perceive their surrounding environment, and as such, major challenges include accurately perceiving the environment, detecting obstacles, and extracting road condition information. Intelligent vehicles utilize sensors to derive images and understand external and internal environments; therefore, sensing and imaging are the foundations for driving intelligent vehicles and smart transportation. Similar technologies can also be utilized to  digitalize road infrastructure.

This Special Issue aims to compile original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of intelligent vehicles and smart transportation.

Potential topics may include but are not limited to:

  • intelligent vehicles;
  • intelligent infrastructure;
  • computer vision;
  • 3D point cloud processing;
  • surrounding situation awareness;
  • autonomous vehicles;
  • smart transportation;
  • smart city;
  • route choice of autonomous vehicles (car, ship, UAV, UAM).

Dr. Lei Zhu
Dr. Hao Xu
Dr. Stanley Young
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

21 pages, 2233 KiB  
Article
Vehicle Recognition and Driving Information Detection with UAV Video Based on Improved YOLOv5-DeepSORT Algorithm
by Binshuang Zheng, Jing Zhou, Zhengqiang Hong, Junyao Tang and Xiaoming Huang
Sensors 2025, 25(9), 2788; https://doi.org/10.3390/s25092788 - 28 Apr 2025
Abstract
To investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) [...] Read more.
To investigate whether the skid resistance of the ramp meets the requirements of vehicle driving safety and stability, the simulation using the ideal driver model is inaccurate. Therefore, considering the driver’s driving habits, this paper proposes the use of Unmanned aerial vehicles (UAVs) for the collection and extraction of vehicle driving information. To process the collected UAV video, the Google Collaboration platform is used to modify and compile the “You Only Look Once” version 5 (YOLOv5) algorithm with Python 3.7.12, and YOLOv5 is retrained with the captured video. The results show that the precision rate P and recall rate R have satisfactory results with an F1 value of 0.86, reflecting a good P-R relationship. The loss function also stabilized at a very low level after 70 training epochs. Then, the trained YOLOv5 is used to replace the Faster R-CNN detector in the DeepSORT algorithm to improve the detection accuracy and speed and extract the vehicle driving information from the perspective of UAV. By coding, the coordinate information of the vehicle trajectory is extracted, the trajectory is smoothed, and the frame difference method is used to calculate the real-time speed information, which is convenient for the establishment of a real driver model. Full article
(This article belongs to the Special Issue Intelligent Vehicle, Infrastructure Perception and Control Based on Imaging and Sensing)
23 pages, 3855 KiB  
Article
Interference Mitigation Using UNet for Integrated Sensing and Communicating Vehicle Networks via Delay–Doppler Sounding Reference Signal Approach
by Yuanqi Tang and Yu Zhu
Sensors 2025, 25(6), 1902; https://doi.org/10.3390/s25061902 - 19 Mar 2025
Viewed by 213
Abstract
Advanced communication systems, particularly in the context of autonomous driving and integrated sensing and communication (ISAC), require high precision and refresh rates for environmental perception, alongside reliable data transmission. This paper presents a novel approach to enhance the ISAC performance in existing 4G [...] Read more.
Advanced communication systems, particularly in the context of autonomous driving and integrated sensing and communication (ISAC), require high precision and refresh rates for environmental perception, alongside reliable data transmission. This paper presents a novel approach to enhance the ISAC performance in existing 4G and 5G systems by utilizing a two-dimensional offset in the Delay–Doppler (DD) domain, effectively leveraging the sounding reference signal (SRS) resources. This method aims to improve spectrum efficiency and sensing accuracy in vehicular networks. However, a key challenge arises from interference between multiple users after the wireless propagation of signals. To address this, we propose a deep learning-based interference mitigation solution using an UNet architecture, which operates on the Range–Doppler maps. The UNet model, with its encoder–decoder structure, efficiently filters out unwanted signals, therefore enhancing the system performance. Simulation results show that the proposed method significantly improves the accuracy of environmental sensing and resource utilization while mitigating interference, even in dense network scenarios. Our findings suggest that this DD-domain-based approach offers a promising solution to optimizing ISAC capabilities in current and future communication systems. Full article
(This article belongs to the Special Issue Intelligent Vehicle, Infrastructure Perception and Control Based on Imaging and Sensing)
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14 pages, 3589 KiB  
Article
Vehicle Localization Using Crowdsourced Data Collected on Urban Roads
by Soohyun Cho and Woojin Chung
Sensors 2024, 24(17), 5531; https://doi.org/10.3390/s24175531 - 27 Aug 2024
Cited by 1 | Viewed by 938
Abstract
Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large [...] Read more.
Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps. Full article
(This article belongs to the Special Issue Intelligent Vehicle, Infrastructure Perception and Control Based on Imaging and Sensing)
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21 pages, 3481 KiB  
Article
Evaluation of Roadside LiDAR-Based and Vision-Based Multi-Model All-Traffic Trajectory Data
by Fei Guan, Hao Xu and Yuan Tian
Sensors 2023, 23(12), 5377; https://doi.org/10.3390/s23125377 - 6 Jun 2023
Cited by 7 | Viewed by 3646
Abstract
Trajectory data has gained increasing attention in the transportation industry due to its capability of providing valuable spatiotemporal information. Recent advancements have introduced a new type of multi-model all-traffic trajectory data which provides high-frequency trajectories of various road users, including vehicles, pedestrians, and [...] Read more.
Trajectory data has gained increasing attention in the transportation industry due to its capability of providing valuable spatiotemporal information. Recent advancements have introduced a new type of multi-model all-traffic trajectory data which provides high-frequency trajectories of various road users, including vehicles, pedestrians, and bicyclists. This data offers enhanced accuracy, higher frequency, and full detection penetration, making it ideal for microscopic traffic analysis. In this study, we compare and evaluate trajectory data collected from two prevalent roadside sensors: LiDAR and camera (computer vision). The comparison is conducted at the same intersection and over the same time period. Our findings reveal that current LiDAR-based trajectory data exhibits a broader detection range and is less affected by poor lighting conditions compared to computer vision-based data. Both sensors demonstrate acceptable performance for volume counting during daylight hours, but LiDAR-based data maintains more consistent accuracy at night, particularly in pedestrian counting. Furthermore, our analysis demonstrates that, after applying smoothing techniques, both LiDAR and computer vision systems accurately measure vehicle speeds, while vision-based data show greater fluctuations in pedestrian speed measurements. Overall, this study provides insights into the advantages and disadvantages of LiDAR-based and computer vision-based trajectory data, serving as a valuable reference for researchers, engineers, and other trajectory data users in selecting the most appropriate sensor for their specific needs. Full article
(This article belongs to the Special Issue Intelligent Vehicle, Infrastructure Perception and Control Based on Imaging and Sensing)
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