Traffic Light Detection and Recognition for Self Driving Cars Using Deep Learning

Self-driving cars has the potential to revolutionize urban mobility by providing sustainable, safe, convenient and congestion free transportability. This vehicle autonomy as an application of AI has several challenges like infallibly recognizing traffic lights, signs, unclear lane markings, pedestrians, etc. These problems can be overcome by using the technological development in the fields of Deep Learning, Computer Vision due to availability of Graphical Processing Units (GPU) and cloud platform. In this paper, we propose a deep neural network based model for reliable detection and recognition of traffic lights using transfer learning. The method incorporates use of faster region based convolutional network (R-CNN) Inception V2 model in TensorFlow for transfer learning. The model was trained on dataset containing different images of traffic signals in accordance with Indian Traffic Signals which are distinguished in five types of classes. The model accomplishes its objective by detecting the traffic light with its correct class type.