PROBABILISTIC AND FUZZY METHODS FOR SENSOR VALIDATION AND FUSION IN VEHICLE GUIDANCE : A COMPARISON

This paper presents a comparison of methods to deal with uncertainty that is associated with longitudinal distance sensors. The methods are based on probabilistic and fuzzy approaches for sensor validation and sensor fusion. The performance of these methods is investigated as they are applied to follower vehicle guidance for platooning tasks in an automated highway setting.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/37652074
• Supplemental Notes:
  • Publication Date: 1997 Published By: Automotive Automation, Croydon, England
• Corporate Authors:

  University of California, Berkeley

  Department of Mechanical Engineering
  Berkeley, CA  United States  94720-1740

  University of California, Berkeley

  California PATH Program, Institute of Transportation Studies
  Richmond Field Station, 1357 South 46th Street
  Richmond, CA  United States  94804-4648

  Utah State University, Logan

  Old Main Hill
  Logan, UT  United States  84322-4110
• Authors:
  • Goebel, Kai
  • Agogino, Alice M
  • Alag, Satnam
• Conference:
  • International Symposium on Automotive Technology & Automation (30th : 1997 : Florence, Italy). Mechatronics/automotive electronics : real world reasons to use unigraphics and iman : proceedings ... Vol. 2
• Publication Date: 1997

Language

• English

Media Info

• Pagination: p. 711-719

Subject/Index Terms

• TRT Terms: Automated highways; Data fusion; Fuzzy logic; Fuzzy systems; In vehicle sensors; Kalman filtering
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 00777221
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Nov 17 1999 12:00AM