MOVING CLUSTER CLASSIFICATION TECHNIQUE WITH LIDAR TRAFFIC MONITORING APPLICATION

In this paper, a dynamic clustering method (DCM) is presented that is able to move laser radar data for an onboard automobile traffic monitoring application. The DCM uses Kalman filters to track the dynamic states of the data points, and a cluster classification and predictor algorithm to identify objects in the information.

• Supplemental Notes:
  • Publication Date: 1998 Published By: American Automatic Control Council, Evanston IL
• Corporate Authors:

  University of California, Irvine

  Department of Electrical and Computer Engineering
  Irvine, CA  United States 

  University of California, Berkeley

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

  Oakland University. Dept. of Electrical and Systems Engineering

  ,    

  Ch'ing hua ta hsueh (Beijing, China)

  ,    

  University of California, Berkeley

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

  University of Michigan, Ann Arbor

  Department of Electrical Engineering and Computer Science
  Ann Arbor, MI  United States  48109

  Ford Motor Company

  Scientific and Research Laboratory
  Dearborn, MI  United States  48124

  University of California, Los Angeles

  Department of Mechanical, Aerospace and Nuclear Engineering
  Los Angeles, CA  United States 
• Authors:
  • Cheok, K C
  • Nishizawa, Shinichi
  • Young, W J
• Conference:
  • American Control Conference (17th : 1998 : Philadelphia, Pa.). Proceedings
• Publication Date: 1998

Language

• English

Media Info

• Pagination: p. 944-949

Subject/Index Terms

• TRT Terms: In vehicle sensors; Kalman filtering; Optical radar; Pattern recognition systems

Filing Info

• Accession Number: 00777006
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: PATH
• Created Date: Nov 17 1999 12:00AM