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Intelligent Transportation System Influences on Driving Behavior and Traffic Safety

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 1231

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; human–machine co-driving; simulated driving
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Autonomous Systems and Robotics Lab, Polytechnic Institute of Paris, 91120 Palaiseau, France
Interests: driving behaviors; human–machine co-driving; human modelling; intelligent safety driving; human factors
Special Issues, Collections and Topics in MDPI journals
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: traffic safety; accident analysis; intelligent and connected vehicles; human factors engineering; ITS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Given the continuous improvement in transportation infrastructure and intelligent connected vehicles, the intelligence of the transportation system is gradually improving. Intelligent transportation systems show new trends and characteristics, such as vehicle telematics, co-operative vehicle infrastructure systems and autonomous driving. Compared to traditional road traffic environments, the perception, cognition and decision-making behaviors of drivers radically changes in an intelligent and connected environment. Intelligent transportation systems present opportunities to improve traffic safety and energy efficiency; however, the growing popularity of intelligent connected vehicles could also cause managing traffic flows to become more complicated.

In addition, due to the increased use of sensing technology in intelligent transportation systems, researchers can conveniently obtain driving behavior and analyze it to determine common road safety issues. Real-world driving behavior can be captured through intelligent connected vehicles, while trajectory data can be obtained through intelligent roadside perception radar or video technology. Therefore, due to recent advancements in sensing technologies, researchers can expand the scope of research into driving behavior and traffic safety.

To manage the changes in intelligent transportation systems, researchers urgently need to study the following issues: sensing technology in intelligent transportation systems, analysis of characteristics of travel and driving behavior in intelligent transportation systems, exploration of the characteristics of traffic flow and risk evolution under intelligent transportation systems, modeling the impact of intelligent transportation technology on transportation systems, and proposals regarding driving behavior intervention and risk control methods in intelligent transportation systems. We invite authors to submit papers addressing these topics for publication in this Special Issue.

You may choose our Joint Special Issue in IJERPH.

Dr. Nengchao Lyu
Prof. Dr. Adriana Tapus
Dr. Quan Yuan
Guest Editors

Manuscript Submission Information

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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

  • intelligent transportation systems
  • sensing technology
  • traffic safety
  • driving behaviors
  • accident analysis
  • intelligent and connected vehicles
  • human factors
  • traffic control

Published Papers (1 paper)

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Research

23 pages, 6934 KiB  
Article
Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection
by Da Xu, Mengfei Liu, Xinpeng Yao and Nengchao Lyu
Sensors 2023, 23(24), 9800; https://doi.org/10.3390/s23249800 - 13 Dec 2023
Cited by 2 | Viewed by 758
Abstract
The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors [...] Read more.
The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes. Full article
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