Adaptive Headlight Control Using Q-Learning Reinforcement Algorithm

In this paper, we introduce an adaptive headlight control system using reinforcement learning to overcome challenges faced in the automobile industry. The project aims to ease visibility issues using an adaptive system, considering the varying climatic conditions during day and night driving. The system uses Q-learning to automatically change the brightness levels to the surrounding environment and classify intermediate brightness levels that were not observed during training, also efficiently classifying recognized “light” and “dark” intensity ranges. This quantitative evaluation of the model's performance demonstrates how flexible and capable it is at making decisions in new situations. The concept shows great promise in improving safety and maximizing energy efficiency in car lighting systems overall.