Intelligent headlight control using learning-based approaches
This paper describes our recent work on developing an intelligent headlight control system using machine learning-based approaches. Specifically, such a system aims to automatically control a vehicle's beam state (high beam or low beam) during a night-time drive based on the detection of oncoming/overtaking/leading traffics as well as urban areas from the videos captured by a camera. Two machine learning-based approaches, namely, support vector machine (SVM) and AdaBoost, have been applied to accomplish this task. The architect of each approach, as well as its detailed processing modules, will be elaborated in the paper. The system has been extensively tested both online and offline to validate the robustness and effectiveness of the two proposed approaches. A detailed performance study along with some comparisons between the two approaches will be reported at the end.