The purpose of design and building autonomous cars is to eliminate the human factor in order to reduce losses and costs and also increase safety by replacing smart equipment. Todays, using artificial intelligence and machine learning, we are witnessing significant advances in the intelligent transportations, especially fully automated vehicles, which are able to analyze environmental information using advanced sensors and machine vision techniques. One of the challenges in designing such systems is a correct identification of other vehicles around the route of the vehicle. In this paper, a deep learning-based method for identifying emergency vehicles is presented in which feature extraction and classification processes are performed simultaneously. The deep network used in this research is a convolutional network. In Convolutional Neural Networks (CNN), achieving acceptable results and proper performance requires having a huge amount of data for network training. Due to the limited number of images in the data set used in this study and in order to increase the identification accuracy, transfer learning process and VGG16 pre-trained network have been used. Two new datasets were created for this study and furthermore two other known datasets were also examined. The proposed method was compared with four other known methods from the literature, where the final results showed supremacy of the proposed approach.