Traffic Light Detection using Convolutional Neural Networks and Lidar Data

This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.