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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: 31 July 2024 | Viewed by 2416

Special Issue Editors

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
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

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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.

Published Papers (1 paper)

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Research

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 - 06 Jun 2023
Cited by 3 | Viewed by 1856
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
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