One of most important technology of automated driving systems is to detect traffic signs in various environmental conditions. Due to various environmental changes it is difficult to detect the traffic signs more accurately. In this project, a deep neural network is used for different traffic sign recognition. The presented method uses convolution neural network which extracts more features from input image dataset to carry out recognition. According to statistics, most road accidents take place due to lack of response time to instant traffic events. With self-driving cars, this problem can be addressed by implementing automated systems to detect these traffic events. This involves correctly identifying the traffic signs that can be faced by an automated vehicle, classifying them, and responding to them. The techniques used by us made our system achieve better accuracy under variable lighting conditions. Sequential model can be built by using the Sequential () class. Here, we sequentially add layers to the model using the add()method. According to the Keras documentation, A filter or a kernel in a conv2D layer has a height and a width. They are generally smaller than the input image and so we move them across the whole image processing may also include color