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Intelligent Traffic Safety and Security

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

Deadline for manuscript submissions: 25 July 2025 | Viewed by 1503

Special Issue Editors

Dr. Pengcheng Wang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: intelligent transportation system; internet of vehicles; cybersecurity on vehicles; autonomous vehicle
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Yang

E-Mail Website
Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: road traffic safety; traffic big data; ITS theory and technology; connected autonomous driving
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangnian Xiao

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
Interests: intelligent transportation; green shipping; sustainable transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of the rapid development of global autonomous driving technology, intelligent traffic safety and security has become a hot research topic. With the popularization of autonomous driving systems, they have shown great potential in improving road safety and optimizing traffic efficiency. However, the security and information confidentiality of intelligent systems have become urgent problems to be solved.

This Special Issue focuses on the core content of automatic driving, intelligent systems and information security, aiming to discuss the latest advancements in automatic driving systems, providing in-depth research in relation to the information security mechanisms of intelligent systems and providing a security guarantee for the development of intelligent transportation.

Dr. Pengcheng Wang
Dr. Yang Yang
Prof. Dr. Guangnian Xiao
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

  • automatic driving
  • intelligent system
  • traffic safety
  • information security
  • intelligent transportation
  • road safety optimization
  • sensor technology

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Published Papers (2 papers)

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27 pages, 6113 KiB  
Article
An Identification Method for Road Hypnosis Based on XGBoost-HMM
by Longfei Chen, Chenyang Jiao, Bin Wang, Xiaoyuan Wang, Jingheng Wang, Han Zhang, Junyan Han, Cheng Shen, Kai Feng, Quanzheng Wang and Yi Liu
Sensors 2025, 25(6), 1842; https://doi.org/10.3390/s25061842 - 16 Mar 2025
Viewed by 364
Abstract
Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver’s psychological [...] Read more.
Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver’s psychological state. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task, and driving safety is greatly affected. Therefore, road hypnosis identification is of great significance for the active safety of vehicles. A road hypnosis identification model based on XGBoost—Hidden Markov is proposed in this study. Driver data and vehicle data related to road hypnosis are collected through the design and conduct of vehicle driving experiments. Driver data, including eye movement data and EEG data, are collected with eye movement sensors and EEG sensors. A mobile phone with AutoNavi navigation is used as an on-board sensor to collect vehicle speed, acceleration, and other information. Power spectrum density analysis, the sliding window method, and the point-by-point calculation method are used to extract the dynamic characteristics of road hypnosis, respectively. Through normalization and standardization, the key features of the three types of data are integrated into unified feature vectors. Based on XGBoost and the Hidden Markov algorithm, a road hypnotic identification model is constructed. The model is verified and evaluated through visual analysis. The results show that the road hypnosis state can be effectively identified by the model. The extraction of road hypnosis-related features is realized in non-fixed driving routes in this study. A new research idea for road hypnosis and a technical scheme reference for the development of intelligent driving assistance systems are provided, and the life identification ability of the vehicle intelligent cockpit is also improved. It is of great significance for the active safety of vehicles. Full article
(This article belongs to the Special Issue Intelligent Traffic Safety and Security)
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29 pages, 16077 KiB  
Article
Traffic Sign Detection and Quality Assessment Using YOLOv8 in Daytime and Nighttime Conditions
by Ziyad N. Aldoski and Csaba Koren
Sensors 2025, 25(4), 1027; https://doi.org/10.3390/s25041027 - 9 Feb 2025
Viewed by 860
Abstract
Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic [...] Read more.
Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article
(This article belongs to the Special Issue Intelligent Traffic Safety and Security)
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