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Trajectory Precise Perception of Traffic Targets and Its Applications

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1260

Special Issue Editors


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Guest Editor
1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2. National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: traffic safety; driving behaviors; intelligent and connected transportation; vehicle trajectory; simulated driving
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Intelligent Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: intelligent vehicles and driver assistance; traffic information and safety; vehicle-to-infrastructure cooperation and safety control; traffic simulation and data mining; new energy vehicles
Special Issues, Collections and Topics in MDPI journals
1. The School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
2. Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan 430063, China
Interests: traffic safety; connected and automated vehicle; intelligent transportation system; big data analytics and statistics

E-Mail Website
Guest Editor
School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
Interests: traffic safety; driving behavior; traffic design; driving simulation; intelligent and connected transportation

Special Issue Information

Dear Colleagues,

Advancements in intelligent in-vehicle and roadside perception technologies have made it possible to acquire detailed perceptual information about target vehicles and surrounding environments. This information includes operational data, motion trajectories, and vehicle-to-vehicle (V2V) interaction data. Such insights can be leveraged to explore drivers' cognitive mechanisms, analyze factors influencing traffic conflict risks, and predict traffic flow dynamics, offering significant practical potential. However, several technical challenges still need to be addressed.

This Special Issue will focus on key theoretical and methodological breakthroughs, including trajectory extraction under occlusion conditions, high-quality perception through multi-sensor fusion, extracting accurate trajectories to identify critical traffic state variables, and predicting and evaluating traffic performance. These developments will provide essential guidance for advancing smart road infrastructure, proactive traffic management strategies, and policy formulation, ultimately contributing to safer, more efficient, and sustainable transportation systems.

Prof. Dr. Nengchao Lyu
Dr. Ronghui Zhang
Dr. Li Song
Dr. Hongliang Wan
Guest Editors

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Keywords

  • roadside sensor
  • in-vehicle sensor
  • vehicle trajectories
  • multi-sensor fusion
  • state identification
  • proactive traffic management

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Published Papers (1 paper)

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Review

35 pages, 2666 KB  
Review
A Review of Methods for Predicting Driver Take-Over Time in Conditionally Automated Driving
by Haoran Wu, Xun Zhou, Nengchao Lyu, Yugang Wang, Linli Xu and Zhengcai Yang
Sensors 2025, 25(22), 6931; https://doi.org/10.3390/s25226931 - 13 Nov 2025
Viewed by 760
Abstract
Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there [...] Read more.
Take-over time is a critical factor affecting safety. Accurately predicting the take-over time provides a more reliable basis on issuing take-over requests, assessment of take-over risks, and optimization of human–machine interaction modes. Although there has been substantial research on predicting take-over time, there are still shortcomings in personalized prediction (particularly in accounting for individual differences in driving experience, cognitive abilities, and physiological responses). To gain a comprehensive understanding of the characteristics and applicability of take-over time prediction methods, this review covers four aspects: literature search information, factors influencing take-over time, data acquisition and processing methods, and take-over time prediction methods. Through literature search, research hotspots in recent years have been summarized, revealing the main research directions and trends. Key factors influencing take-over time, including driver factors, autonomous driving systems, and driving environments, are discussed. Data preprocessing stages, including data acquisition and processing, are systematically analyzed. The advantages and disadvantages of classical statistical, machine learning, and cognitive architecture models are summarized, and the shortcomings in current research are highlighted (for instance, the limited generalizability of models trained predominantly on simulator data to real-world driving scenarios). By thoroughly summarizing the strengths and weaknesses of existing research, this review explores under-researched areas and future trends, aiming to provide a solid theoretical foundation and innovative research perspectives for optimizing take-over time prediction, thereby promoting the widespread application and efficient development of autonomous driving technology. Full article
(This article belongs to the Special Issue Trajectory Precise Perception of Traffic Targets and Its Applications)
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