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Electronics
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Advanced Technologies in Intelligent Transportation Systems
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Advanced Technologies in Intelligent Transportation Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 7617

Special Issue Editors

Dr. Thomas Win

E-Mail Website
Guest Editor
Department of Computer Science and Creative Technologies, College of Arts, Technology and Environment, University of the West of England (UWE) Bristol, Bristol BS16 1QY, UK
Interests: cloud security; cyber physical systems security; virtualisation security
Dr. Djamel Djenouri

E-Mail Website
Guest Editor
Computer Science Research Centre, Department of Computer Science and Creative Technology, University of the West of England, Bristol BS16 1QY, UK
Interests: wireless sensor networks; Internet of Things; smart city applications; mobile networks; distributed systems; network security; smart buildings

Special Issue Information

Dear Colleagues,

Recent endeavours in smart city initiatives have seen an increased proliferation and adoption of the Internet of Things (IoT), cloud computing and other modern computing technologies. Their use has been particularly prominent in the development of smart transport, particularly in intelligent transport systems (ITS) and connected autonomous vechicles (CAV). Using various sensors connected to the vehicles, data are collected which relate to different aspects of smart transport, such as geographical data and driver information, through edge computing. These data are then analysed using cloud computing-based machine learning. The collected data usually contain personally identifiable information (PII) such as user names and timestamps. These can be triangulated to reveal sensitive user details and behaviour.

Despite their increasing adoption, these methods remain limited in their computational capabilities and require the integration of other technologies to conduct data analysis and processing. These deficiencies have resulted in an increased volume of available attack surfaces for targeted cybersecurity attacks. These incidents range from distributed denial of service (DDoS) to ransomware attacks.

The aim is to compile articles exploring the countermeasures against cybersecurity and data privacy attacks in ITS. These can include traditional research-focused articles; educational surveys to instruct the broader technical community; informative articles that discuss practical policy, legal, and economic aspects of cybersecurity in ITS systems.

Dr. Thomas Win
Dr. Djamel Djenouri
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. Electronics 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 2400 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

  • smart transport
  • Industry 4.0
  • cyber–physical systems
  • intelligent transport systems

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Published Papers (4 papers)

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Research

27 pages, 5536 KiB  
Article
Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather
by Lu Wen, Yongliang Peng, Miao Lin, Nan Gan and Rongqing Tan
Electronics 2024, 13(1), 220; https://doi.org/10.3390/electronics13010220 - 3 Jan 2024
Cited by 5 | Viewed by 2197
Abstract
Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. [...] Read more.
Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. In this paper, a multi-modal contrastive learning (CL) strategy, named DHT-CL, is proposed to improve point cloud 3DSS in complex weather for rail-obstacle detection. DHT-CL is a camera and LiDAR sensor fusion strategy specifically designed for complex weather and obstacle detection tasks, without the need for image input during the inference stage. We first demonstrate how the sensor fusion method is more robust under rainy and snowy conditions, and then we design a Dual-Helix Transformer (DHT) to extract deeper cross-modal information through a neighborhood attention mechanism. Then, an obstacle anomaly-aware cross-modal discrimination loss is constructed for collaborative optimization that adapts to the anomaly identification task. Experimental results on a complex weather railway dataset show that with an mIoU of 87.38%, the proposed DHT-CL strategy achieves better performance compared to other high-performance models from the autonomous driving dataset, SemanticKITTI. The qualitative results show that DHT-CL achieves higher accuracy in clear weather and reduces false alarms in rainy and snowy weather. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Transportation Systems)
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26 pages, 1683 KiB  
Article
Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques
by Mohammed A. Khasawneh and Anjali Awasthi
Electronics 2023, 12(24), 4968; https://doi.org/10.3390/electronics12244968 - 11 Dec 2023
Cited by 1 | Viewed by 2305
Abstract
This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and [...] Read more.
This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Transportation Systems)
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17 pages, 8323 KiB  
Article
Post-Impact Stabilization during Lane Change Maneuver
by Yeayoung Park, Juhui Gim and Changsun Ahn
Electronics 2023, 12(22), 4712; https://doi.org/10.3390/electronics12224712 - 20 Nov 2023
Viewed by 919
Abstract
This study addresses challenges in vehicle collisions, especially in non-front or non-rear impacts, causing rapid state changes and a loss of control. Electronic Stability Control (ESC) can stabilize a vehicle in minor impact cases, but it cannot effectively handle major collision cases. To [...] Read more.
This study addresses challenges in vehicle collisions, especially in non-front or non-rear impacts, causing rapid state changes and a loss of control. Electronic Stability Control (ESC) can stabilize a vehicle in minor impact cases, but it cannot effectively handle major collision cases. To overcome this, our research focuses on Post-Impact Stabilization Control (PISC). Existing PISC methods face issues like misidentifying collisions during cornering maneuvers due to assumptions of straight driving, rendering them ineffective for lane change accidents. Our study aims to design PISC specifically for cornering and lane change maneuvers, predicting collision forces solely from the ego vehicle’s data, ensuring improved collision stability control. We employ the unscented Kalman filter to estimate collision forces and develop a sliding mode controller with an optimal force allocation algorithm to counter the disturbances caused by collisions and stabilize the vehicle. Rigorous validation through simulations and tests with a driving simulator demonstrates the feasibility of our proposed methodology in effectively stabilizing vehicles during collision accidents, particularly in lane change situations. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Transportation Systems)
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18 pages, 3556 KiB  
Article
Construction of Personalized Bus Travel Time Prediction Intervals Based on Hierarchical Clustering and the Bootstrap Method
by Zhenzhong Yin and Bin Zhang
Electronics 2023, 12(8), 1917; https://doi.org/10.3390/electronics12081917 - 19 Apr 2023
Cited by 6 | Viewed by 1574
Abstract
Providing accurate bus travel time information is very important to help passengers plan their trips and reduce waiting times. Due to the uncertainty of the bus travel time, the traditional prediction value of the travel time point cannot accurately describe the reliability of [...] Read more.
Providing accurate bus travel time information is very important to help passengers plan their trips and reduce waiting times. Due to the uncertainty of the bus travel time, the traditional prediction value of the travel time point cannot accurately describe the reliability of the prediction result, which is not conducive to passengers waiting for the bus according to the prediction result. At the same time, due to the large differences in the individual driving styles of the bus drivers, the travel time data fluctuate greatly, and the accuracy and reliability of the point prediction results are further reduced. To address this issue, this study develops a personalized bus travel time prediction intervals model for different drivers based on the bootstrap method. Personalized travel time prediction intervals were constructed for drivers with different driving styles. To further improve the quality of travel time prediction intervals, this study optimizes training data sets considering driving style factors. Then, this paper integrates hierarchical clustering, an artificial neural network, and the bootstrap method to construct another prediction intervals model for bus travel time based on driver driving style clustering and the bootstrap method. The real−world driving data sets of the No. 239 bus in Shenyang, China, were used for experimental verification. The results showed that the two models constructed in this paper can effectively quantify the uncertainty of the point prediction results, the PICP of each interval exceeding the confidence level set (80%). It was also found that the quality of the prediction intervals constructed by clustering the driving style data is better (MPIW values decreased by 23.33%, 54.24%, and 28.61 respectively, and the corresponding NMPIW values also decreased by 18.93%, 10.39%, and 14.19%, respectively), which can provide passengers with more reasonable suggestions for waiting time. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Transportation Systems)
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