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A Novel Method for Driving Condition Recognition Based on Compressed Sensing

[+] Author Affiliations
Xing Zhang, Zuomin Dong, Curran Crawford

University of Victoria, Victoria, BC, Canada

Paper No. DETC2013-13202, pp. V001T01A014; 10 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 15th International Conference on Advanced Vehicle Technologies; 10th International Conference on Design Education; 7th International Conference on Micro- and Nanosystems
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5584-3
  • Copyright © 2013 by ASME


Much research has been conducted in the area of driving condition recognition, which is adopted in the control system of hybrid electric vehicle (HEV) and driving assistance system of both alternative energy and conventional vehicle. In this manuscript, Compressed Sensing will be firstly used to improve the efficiency of vehicle speed sampling, then Support Vector machine will be employed to classify the results of Compressed Sensing into several driving condition types. Finally, the recognition results will be compared with traditional driving condition recognition methods (without Compressed Sensing) and conclusion can be drawn that Compressed Sensing can not only increase the efficiency of vehicle speed sampling, but also improve the classification accuracy.

Copyright © 2013 by ASME



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