End-to-End Deep Learning Model for Steering Angle Control of Autonomous Vehicles

Recently brilliant evolutions in the machine learning research area of autonomous self-driving vehicles. Unlike a modern rule-based method, this study has been supervised on the manipulate of images end-to-end, which is deep learning. The motivation of this paper where the input to the model is the camera image and the output is the steering angle target. The model trained a Residual Neural Network (ResNet) convolutional neural network (CNN) algorithm to drive an autonomous vehicle in the simulator. Therefore, the model trained and simulation are conducted using the UDACITY platform. The simulator has two choices one is the training and the second one is autonomous. The autonomous has two tracks track ₁ considered as simple and track ₂ complex as compare to track₁. In our paper, we used track₁ for autonomous driving in the simulator. The training option gives the recorded dataset its control through the keyboard in the simulator. We collected about 11655 images (left, center, right) with four attributes (steering, throttle, brake, speed) and also images dataset stored in a folder and attributes dataset save as CSV file in the same path. The stored raw images and steering angle data set used in this method. We divided 80-20 data set for training and Validation as shown in Table I. Images were sequentially fed into the convolutional neural network (ResNet) to predict the driving factors for making end planning decisions and execution of autonomous motion of vehicles. The loss value of the proposed model is 0. 0418 as shown in Figure 2. The method trained takes succeeded precision of 0. 81% is good consent with expected performance.