A Robust Active Safety Enhancement Strategy With Learning Mechanism in Vehicular Networks

Driving safety has been a hot topic in recent vehicular research. However, research on active control strategy, by which an accident might be avoided before it really happens, is still lacking, especially those appealing to machine learning methods with real traffic data. In addition, previous works constructed models with only one or a few factors considered, while the impact of multiple factors on a collision probability is overlooked. In this paper, based on machine learning methods with an actual traffic dataset, the authors propose a multi-level active safety control strategy taking the Multi-source, Multi-parameter, and Multi-purpose (3M) properties of an accident into consideration. First, by analyzing the impact of different conditions on an accident with the AHP (Analytic Hierarchy Process)-Ridge regression and bisecting K-means clustering model, the safety inter-vehicle distance is derived by learning from an actual traffic dataset. Besides, ELM (Extreme Learning Machines) is adopted as a verification scheme for safety distance calculation. Subsequently, the authors design a three-level active safety control scheme using the LQG (Linear Quadratic Gaussian) optimal-control model based on the obtained safety inter-vehicle distance. Numerical results show that by comparing with some classical braking and car-following models, the authors' strategy can always keep the distance of two followed vehicles at a safety state. To further explore the impact of the time complexity on the rear-end collisions, the authors also implemented a road-test and verified that their model can timely respond to the risks and keep two cars always in safety.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2019.2949432
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Chen, Chen
  • Wang, Cong
  • Qiu, Tie
  • Xu, Zhigang
  • Song, Houbing
• Publication Date: 2020-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 5160-5176
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 21
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Active safety systems; Cluster analysis; Machine learning; Regression analysis; Traffic actuated controllers; Vehicle spacing; Vehicular ad hoc networks; Warning systems
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01761875
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 31 2020 4:59PM