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Intelligent Transportation Systems: Advances in Object Detection and Traffic Management
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Intelligent Transportation Systems: Advances in Object Detection and Traffic Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 1482

Special Issue Editors

Prof. Dr. John E. Ball

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, 406 Hardy Road, 216 Simrall Hall, Mississippi State, MS 39762, USA
Interests: Advanced Driver Assistance Systems (ADAS); scene understanding; sensor processing (Radar, LiDAR, camera, thermal); machine learning; digital image and signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Simegnew Alaba

E-Mail Website
Guest Editor
College of Engineering, Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: computer vision; deep learning; machine learning; autonomous driving

Special Issue Information

Dear Colleagues, 

The rapid development of machine learning, deep learning, computer vision, and advanced sensor technologies has led to significant advancements in intelligent transportation systems (ITSs). These innovations have transformed traffic management and object detection, contributing to safer and more efficient transportation networks. The application of AI-driven techniques, including machine learning and deep learning, is at the forefront of this transformation, leading to substantial improvements in areas such as smart cities, autonomous vehicles, and traffic monitoring systems. This Special Issue aims to highlight the latest advancements and innovative solutions that address urban mobility and traffic safety challenges by integrating these cutting-edge technologies.

Topics to be covered include, but are not limited to, the following, aimed at enhancing intelligent transportation systems:·       

  • Advanced driver assistance systems (ADASs), including automony modes such as collision avoidance, adaptive cruise control, lateral control, lane following, intersection navigation, auto parking, etc.;·        
  • Deep learning applications in ITSs;·        
  • Machine learning for traffic prediction and management;·        
  • Efficient object detection models;·        
  • Robust object and obstacle detection;·        
  • Explaninable AI in automy applications;·        
  • Sensor technologies and processing for ITSs;·        
  • Digital signal processing in transportation systems;·        
  • Digital image processing for traffic monitoring;·        
  • Real-time traffic management solutions;·        
  • Autonomous vehicle technologies;·        
  • Traffic safety and security techniques;·        
  • Computationally efficient algorithms for ITSs;·        
  • Computer vision.

We look forward to receiving your contributions.

Prof. Dr. John E. Ball
Dr. Simegnew Alaba
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

  • autonomous vehicles
  • deep learning
  • efficient transportation system
  • intelligent transportation systems
  • object detection
  • smart cities
  • sensors
  • fusion
  • traffic safety

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Published Papers (2 papers)

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16 pages, 5093 KiB  
Article
Research on Trajectory Planning Method Based on Bézier Curves for Dynamic Scenarios
by Hongluo Li, Hai Pang, Hongyang Xia, Yongxian Huang and Xiangkun Zeng
Electronics 2025, 14(3), 494; https://doi.org/10.3390/electronics14030494 - 25 Jan 2025
Viewed by 465
Abstract
With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, [...] Read more.
With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Advances in Object Detection and Traffic Management)
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17 pages, 2114 KiB  
Article
Research on a Passenger Flow Prediction Model Based on BWO-TCLS-Self-Attention
by Sheng Liu, Lang Du, Ting Cao and Tong Zhang
Electronics 2024, 13(23), 4849; https://doi.org/10.3390/electronics13234849 - 9 Dec 2024
Viewed by 582
Abstract
In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This [...] Read more.
In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This shift has brought increasing attention to the safety of personnel flow in deep spaces. In addressing challenges in deep space passenger flow prediction, such as irregular flow patterns, surges in extreme conditions, large data dimensions, and redundant features complicating the model, this paper proposes a deep space passenger flow prediction model that integrates a Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) network. The model first employs a dual-layer LSTM network structure with a Dropout layer to capture complex temporal dynamics while preventing overfitting. Then, a Self-Attention mechanism and TCN network are introduced to reduce redundant feature data and enhance the model’s performance and speed. Finally, the Beluga Whale Optimization (BWO) algorithm is used to optimize hyperparameters, further improving the prediction accuracy of the network. Experimental results demonstrate that the BWO-TCLS-Self-Attention model proposed in this paper achieves an R2 value of 96.94%, with MAE and RMSE values of 118.464 and 218.118, respectively. Compared with some mainstream prediction models, the R2 value has increased, while both MAE and RMSE values have decreased, indicating its ability to accurately predict passenger flow in deep underground spaces. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Advances in Object Detection and Traffic Management)
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