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Advanced Sensing and Analysis Technology in Transportation Safety

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1285

Special Issue Editors


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Guest Editor
Virginia Tech Transportation Institute, Blacksburg, VA 24060, USA
Interests: deep learning; transportation safety; pavement design and management; 2D/3D imaging sensors
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Virginia Tech, Blacksburg, VA 24061, USA
Interests: vehicle dynamics simulation and testing; development of advanced systems for improved vehicle dynamic performance and safety; advanced sensing technology for railway health monitoring

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Guest Editor
Affiliation: School of Transportation Engineering, Nanjing University of Technology, Nanjing 211106, China
Interests: three-dimensional laser detection technology for road pavements and track surfaces; binocular holographic sensing technology for transportation infrastructure; comprehensive diagnosis of road and track safety performance

E-Mail Website
Guest Editor
Virginia Tech Transportation Institute, Blacksburg, VA 24060, USA
Interests: transportation engineering; heavy vehicle safety; automation; ADAS; bus safety

Special Issue Information

Dear Colleagues,

In recent years, the transportation sector has witnessed significant advancements in sensing technologies (such as camera, radar, LiDAR, and analysis technologies), including machine learning, deep learning, large language models (LLM), and sensor fusion. The focus has been on enhancing traffic safety and operational efficiency for road and rail transportation by implementing advanced technologies. Particularly, the “Safe Systems” approach represents a paradigm shift in how we conceptualize road safety, emphasizing the creation of ‘forgiving’ road environments that prioritize the protection of all road users, including trains, other motorized vehicles, and non-motorized users (e.g., pedestrians). Central to this philosophy are the pillars of safe transportation infrastructure, advanced vehicle technologies, speed management and traffic law enforcement, road users, and post-crash care.

This Special Issue, titled "Advanced Sensing and Analysis Technology in Transportation Safety", sheds light on the innovative research endeavors of researchers, practitioners, and policymakers dedicated to bolstering the principles of “Safe Systems” within the realm of transportation. From the design and implementation of intelligent roadway countermeasures to the integration of sophisticated sensing systems into vehicles, the contributions within this Special Issue represent a concerted effort to mitigate the risks associated with transportation and reduce the incidence of fatalities and serious injuries. Through the exploration of these research endeavors, this Special Issue seeks to advance our understanding of “Safe Systems” in transportation and catalyze the development and implementation of transformative technologies that promise to create safer and smarter transportation environments.

We particularly welcome multidisciplinary contributions in novel sensor and analytical applications to improve transportation safety, with potential submission topics including the following:

  • Technologies to improve vehicle safety, such as automated and connected vehicles, automated driving systems (ADSs) for on-road testing, vehicle-to-everything (V2X) communication, advanced driver assistance systems (ADASs), advanced sensing technologies and control systems to improve overall vehicle performance, driver monitoring, and warning systems.
  • Studies to improve roadway safety, including new countermeasures, work zone safety, intersection safety, pavement friction study, etc.
  • Sensing system development and integration of advanced technologies to improve safety in railway operation, with a focus on rail infrastructure inspection and maintenance, train inspection and maintenance, train operation and control, signaling, and communication.
  • Sensor applications on better speed management to reduce crash risks or corresponding severities, such as speed management in work zones, speed monitoring, dynamic speed limit systems, and intelligent speed adaption.
  • Efforts to improve Vulnerable Road User (VRU) safety, e.g., VRU data collection, VRU detection, VRU safety measures, intersection safety for VRUs, and VRU behavior analysis.
  • The development and assessment of new technologies for on-site crash victim assessment and stabilization, the use of drones for the rapid delivery of medical supplies to accident scenes, automated crash notification systems, telemedicine, and remote support.

Dr. Guangwei Yang
Dr. Yang Chen
Dr. Wenting Luo
Dr. Steven Stapleton
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

  • transportation safety
  • sensing technologies
  • artificial intelligence
  • road infrastructure
  • vehicle safety
  • speed management
  • VRU safety
  • post-crash care

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Published Papers (2 papers)

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Research

18 pages, 6312 KiB  
Article
Mitigating Container Damage and Enhancing Operational Efficiency in Global Containerisation
by Sergej Jakovlev, Tomas Eglynas, Mindaugas Jusis, Valdas Jankunas and Miroslav Voznak
Sensors 2025, 25(7), 2019; https://doi.org/10.3390/s25072019 - 24 Mar 2025
Viewed by 396
Abstract
The global containerisation industry, while significantly advancing international trade, faces persistent challenges related to infrastructure capacity, environmental impact, and operational efficiency. One critical yet under-researched issue is the physical damage that containers endure during handling operations, particularly at port terminals. This paper examines [...] Read more.
The global containerisation industry, while significantly advancing international trade, faces persistent challenges related to infrastructure capacity, environmental impact, and operational efficiency. One critical yet under-researched issue is the physical damage that containers endure during handling operations, particularly at port terminals. This paper examines the complexities of container handling, focusing on damage caused by quay crane activities, especially during corner hooking. Such damage compromises container integrity, impacts cargo safety, and increases operational costs. To address these concerns, we present the Impact Detection Methodology (IDM), a system designed to monitor and detect impacts in real time, enhancing operational precision and safety. Preliminary studies conducted at Klaipeda City port demonstrate the IDM’s effectiveness, though limited data have constrained validation. Our research underscores the need for broader experimentation to confirm the IDM’s potential in mitigating container damage. Key findings indicate that unsuccessful hooking attempts predominantly occur when containers are lifted from above-deck positions, influenced by spreader oscillations and high operational workloads. This paper also highlights the importance of integrating sway control systems with existing crane management technologies to assist operators in reducing handling errors. Enhanced monitoring and data analysis are essential for improving container handling processes, supporting sustainable growth in global containerisation, and mitigating financial risks. Full article
(This article belongs to the Special Issue Advanced Sensing and Analysis Technology in Transportation Safety)
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28 pages, 13455 KiB  
Article
DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data
by Haoran Zhou, Alexander Carballo, Masaki Yamaoka, Minori Yamataka, Keisuke Fujii and Kazuya Takeda
Sensors 2025, 25(6), 1699; https://doi.org/10.3390/s25061699 - 9 Mar 2025
Viewed by 445
Abstract
Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address [...] Read more.
Driving Under the Influence (DUI) has emerged as a significant threat to public safety in recent years. Despite substantial efforts to effectively detect DUI, the inherent risks associated with acquiring DUI-related data pose challenges in meeting the data requirements for training. To address this issue, we propose DUIncoder, which is an unsupervised framework designed to learn exclusively from normal driving data across diverse scenarios to detect DUI behaviors and provide explanatory insights. DUIncoder aims to address the challenge of collecting DUI data by leveraging diverse normal driving data, which can be readily and continuously obtained from daily driving. Experiments on simulator data show that DUIncoder achieves detection performance superior to that of supervised learning methods which require additional DUI data. Moreover, its generalization capabilities and adaptability to incremental data demonstrate its potential for enhanced real-world applicability. Full article
(This article belongs to the Special Issue Advanced Sensing and Analysis Technology in Transportation Safety)
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