In this paper, we first present the architecture of an intelligent headlight control (IHC) system that we developed in our earlier work. This IHC system aims to automatically control a vehicle's beam state (high beam or low beam) during a night-time drive. A three-level decision framework built around a support vector machine (SVM) learning engine is then briefly discussed. Next, we switch our focus to the study of system performance by varying the SVM feature set, as well as by exploiting various SVM training options and adjustments through a set of experiments. We believe that what we learned from this performance study can provide readers useful guidelines on extracting effective SVM features within the IHC problem domain, as well as on training an effective SVM learning engine for more generalized applications.
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