Embedded feature-selection support vector machine for driving pattern recognition

A more efficient and robust driving pattern recognition technique, extended Support Vector Machine with embedded feature selection ability, has been introduced that takes into account the accessibility and reliability of features during feature selection so as to enable the driving condition discrimination system to achieve higher recognition efficiency and robustness.

Abstract

In this work, a more efficient and robust driving pattern recognition technique, extended Support Vector Machine (SVM) with embedded feature selection ability, has been introduced. Besides statistical significance, this proposed SVM also takes into account the accessibility and reliability of features during feature selection, so as to enable the driving condition discrimination system to achieve higher recognition efficiency and robustness. The recognition results of this extended SVM are compared with results from standard 2-norm SVM and linear 1-norm SVM, using representative driving cycle data to demonstrate the function and superiority of the new technique.