Controlling Steering Angle for Cooperative Selfdriving Vehicles utilizing CNN and LSTMbased Deep Networks A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent stateoftheart solutions addressing this challenge include deep learning techniques as they provide endtoend solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically by using LongShortTermMemory (LSTM) in our deep architecture. Our deep architecture is an endtoend network that utilizes CNN, LSTM and fully connected (FC) layers and it uses both present and futures images (shared by a vehicle ahead via VehicletoVehicle (V2V) communication) as input to control the steering angle. Our model demonstrates the lowest error when compared to the other existing approaches in the literature.

EndtoEnd Driving Model for Steering Control of Autonomous Vehicles with Future Spatiotemporal Features Endtoend deep learning has gained considerable interests in autonomous driving vehicles in both academic and industrial fields, especially in decision making process. One critical issue in decision making process of autonomous driving vehicles is steering control. Researchers has already trained different artificial neural networks to predict steering angle with frontfacing camera data stream. However, existing endtoend methods only consider the spatiotemporal relation on a single layer and lack the ability of extracting future spatiotemporal information. In this paper, we propose an endtoend driving model based on Convolutional Long ShortTerm Memory (ConvLSTM) neural network with a Multiscale Spatiotemporal Integration (MSI) module, which aiming to encode the spatiotemporal information from different scales for steering angle prediction. Moreover, we employ future sequential information to enhance spatiotemporal features of the endtoend driving model. We demonstrate the efficiency of proposed endtoend driving model on the public Udacity dataset with comparison of some existing methods. Experimental results show that the proposed model has better performances than other existing methods, especially in some complex scenarios. Furthermore, we evaluate the proposed driving model on a realtime autonomous vehicle, and results show that the proposed driving model is able to predict the steering angle with high accuracy compared to skilled human driver.