A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS

Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.

• Record URL:
  https://doi.org/10.1155/2017/9620831
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2017 Rui Sun et al.
• Authors:
  • Sun, R
  • Xie, F
  • Xue, D
  • Zhang, Yong
  • Ochieng, W Y
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2017
  • Issue Number: Article ID 9620831
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Automatic vehicle identification; Car following; Crash avoidance systems; Fuzzy systems; Global Positioning System; Rear end crashes
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01664745
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2018 9:32AM