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AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems
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AI-Enhanced Sensor Technologies for Traffic Safety 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: closed (31 December 2024) | Viewed by 5796

Special Issue Editor

Dr. Tamer M. Nadeem

E-Mail Website
Guest Editor
Department of Computer Science, Virginia Commonwealth University, 401 W. Main Street, Room ERB 2330, Richmond, VA 23284, USA
Interests: smart wireless systems; mobile and edge computing; AI for networks and systems; software-defined networks; network security and privacy; Internet-of-things and smart city systems; vehicular networks; intelligent transportation systems; and location determination systems.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of urban populations and the increasing demand for efficient transportation networks have brought traffic safety and intelligent transportation systems (ITSs) to the forefront of modern infrastructure development. As cities strive to become smarter and more sustainable, advanced sensor technologies play a critical role in enhancing traffic safety, managing congestion, and improving the overall efficiency of transportation.

Traffic safety is a paramount concern in urban planning, with the aim of reducing accidents, fatalities, and injuries on the road. Advanced sensors provide real-time data that support a wide range of applications, including traffic monitoring, vehicle-to-infrastructure communication, autonomous driving, and the protection of pedestrians. By integrating sensor technologies with advancements in artificial intelligence (AI) and machine learning (ML), we can develop robust systems that significantly enhance the safety and efficiency of traffic.

AI and ML have the potential to transform ITSs by enabling the development of predictive analytics, real-time decision-making, and adaptive control mechanisms. These technologies can analyze vast amounts of data from sensors to identify patterns, predict traffic conditions, and implement proactive measures to mitigate risks and optimize traffic flow.

This Special Issue, entitled “AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems”, seeks to gather cutting-edge research and practical insights into the development, deployment, and evaluation of AI-driven sensor-based systems that have been designed to enhance traffic safety. We welcome submissions that explore novel sensor technologies, AI/ML algorithms, system designs, and applications focused on enhancing road safety and promoting intelligent transportation solutions.

The scope of this Special Issue includes, but is not limited to, the following:

  • Advanced traffic monitoring and control systems;
  • Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication;
  • Autonomous and connected vehicle technologies;
  • Pedestrian and cyclist safety systems;
  • Intelligent traffic signal control and optimization;
  • Real-time traffic data collection and analytics;
  • Edge computing applications in transportation;
  • AI and machine learning for traffic prediction and management;
  • Sensor fusion and data integration for ITS;
  • Environmental sensing for transportation applications;
  • Cybersecurity and privacy in ITS;
  • Energy-efficient sensor networks for transportation;
  • Testbeds and experimental evaluations of ITS solutions;
  • Smart parking systems and management;
  • IoT applications for public transportation;
  • Human factors and user interfaces in intelligent transportation systems.

We invite researchers and practitioners from academia, industry, and government to contribute their original research and review articles to this Special Issue. By sharing innovative approaches and practical solutions, we aim to advance the field of traffic safety and intelligent transportation systems, ultimately creating safer and more efficient urban environments.

Dr. Tamer M. Nadeem
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

  • traffic monitoring
  • vehicle-to-vehicle
  • V2V
  • vehicle-to-infrastructure
  • V2I
  • autonomous and connected vehicles
  • pedestrian and cyclist safety
  • intelligent traffic
  • traffic prediction
  • smart parking
  • intelligent transportation systems

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Published Papers (4 papers)

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Research

22 pages, 4539 KiB  
Article
Resource-Efficient Design and Implementation of Real-Time Parking Monitoring System with Edge Device
by Jungyoon Kim, Incheol Jeong, Jungil Jung and Jinsoo Cho
Sensors 2025, 25(7), 2181; https://doi.org/10.3390/s25072181 - 29 Mar 2025
Viewed by 214
Abstract
Parking management systems play a crucial role in addressing parking shortages and operational challenges; however, high initial costs and infrastructure requirements often hinder their implementation. Edge computing offers a promising solution by reducing latency and network traffic, thus optimizing operational costs. Nonetheless, the [...] Read more.
Parking management systems play a crucial role in addressing parking shortages and operational challenges; however, high initial costs and infrastructure requirements often hinder their implementation. Edge computing offers a promising solution by reducing latency and network traffic, thus optimizing operational costs. Nonetheless, the limited computational resources of edge devices remain a significant challenge. This study developed a real-time vehicle occupancy detection system utilizing SSD-MobileNetv2 on edge devices to process video streams from multiple IP cameras. The system incorporates a dual-trigger mechanism, combining periodic triggers and parking space mask triggers, to optimize computational efficiency and resource usage while maintaining high accuracy and reliability. Experimental results demonstrated that the parking space mask trigger significantly reduced unnecessary AI model executions compared to periodic triggers, while the dual-trigger mechanism ensured consistent updates even under unstable network conditions. The SSD-MobileNetv2 model achieved a frame processing time of 0.32 s and maintained robust detection performance with an F1-score of 0.9848 during a four-month field validation. These findings validate the suitability of the system for real-time parking management in resource-constrained environments. Thus, the proposed smart parking system offers an economical, viable, and practical solution that can significantly contribute to developing smart cities. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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19 pages, 22240 KiB  
Article
Enhanced Broad-Learning-Based Dangerous Driving Action Recognition on Skeletal Data for Driver Monitoring Systems
by Pu Li, Ziye Liu, Hangguan Shan and Chen Chen
Sensors 2025, 25(6), 1769; https://doi.org/10.3390/s25061769 - 12 Mar 2025
Viewed by 408
Abstract
Recognizing dangerous driving actions is critical for improving road safety in modern transportation systems. Traditional Driver Monitoring Systems (DMSs) often face challenges in terms of lightweight design, real-time performance, and robustness, especially when deployed on resource-constrained embedded devices. This paper proposes a novel [...] Read more.
Recognizing dangerous driving actions is critical for improving road safety in modern transportation systems. Traditional Driver Monitoring Systems (DMSs) often face challenges in terms of lightweight design, real-time performance, and robustness, especially when deployed on resource-constrained embedded devices. This paper proposes a novel method based on 3D skeletal data, combining Graph Spatio-Temporal Feature Representation (GSFR) with a Broad Learning System (BLS) to overcome these challenges. The GSFR method dynamically selects the most relevant keypoints from 3D skeletal data, improving robustness and reducing computational complexity by focusing on essential driver movements. The BLS model, optimized with sparse feature selection and Principal Component Analysis (PCA), ensures efficient processing and real-time performance. Additionally, a dual smoothing strategy, consisting of sliding window smoothing and an Exponential Moving Average (EMA), stabilizes predictions and reduces sensitivity to noise. Extensive experiments on multiple public datasets demonstrate that the GSFR-BLS model outperforms existing methods in terms of accuracy, efficiency, and robustness, making it a suitable candidate for practical deployment in embedded DMS applications. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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16 pages, 4344 KiB  
Article
Multi-Scale Spatio-Temporal Attention Networks for Network-Scale Traffic Learning and Forecasting
by Cong Wu, Hui Ding, Zhongwang Fu and Ning Sun
Sensors 2024, 24(17), 5543; https://doi.org/10.3390/s24175543 - 27 Aug 2024
Cited by 2 | Viewed by 3426
Abstract
Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and [...] Read more.
Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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26 pages, 12387 KiB  
Article
Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution
by Hongping Wang, Xin Liu, Linsen Song, Yiwen Zhang, Xin Rong and Yitian Wang
Sensors 2024, 24(15), 4951; https://doi.org/10.3390/s24154951 - 30 Jul 2024
Cited by 1 | Viewed by 1116
Abstract
This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as [...] Read more.
This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks. Full article
(This article belongs to the Special Issue AI-Enhanced Sensor Technologies for Traffic Safety and Intelligent Transportation Systems)
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