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.

This content is only available via PDF.
You do not currently have access to this content.