Intelligent Traffic Light System using Deep Reinforcement Learning

Abstract

Currently, population growth in cities results in an increase in urban vehicle traffic. That is why it is necessary to improve the quality of life of citizens based on the improvement of transport control services. To solve this problem, there are solutions, related to the improvement of the road infrastructure by increasing the roads or paths. One of the solutions is using traffic lights that allow traffic regulation automatically with machine learning techniques. That is why the implementation of an intelligent traffic light system with automatic learning by reinforcement is proposed to reduce vehicular and pedestrian traffic. As a result, the use of the YOLOv4 tool allowed us to adequately count cars and people, differentiating them based on size and other characteristics. On the other hand, the position of the camera and its resolution is a key point for counting vehicles by detecting their contour. An improvement in time has been obtained using reinforcement learning, which depends on the number of episodes analyzed and affects the length of training time, where the analysis of 100 episodes takes around 12 hours on a Ryzen 7 computer with a graphics card built-in 2 GB.