DeepAoA: Online Vehicular Direction Finding Based on a Deep Learning Method Relative direction estimation among neighboring vehicles in urban environment is essential to a wide variety of driving safety applications. To obtain accurate direction information solely from vehicletovehicle (V2V) communications is desirable but very challenging due to the highly dynamic vehicular environments. In this paper, we propose an online vehicular AoA estimation scheme, called DeepAoA, based on a deep learning method. More specifically, Channel state information (CSI) is estimated from a set of synchronized receiving radios by a receiver vehicle. By taking the CSI phase difference between a pair of such radios, CSI phase errors in baseband can be effectively eliminated, which makes CSI phase difference a compelling feature to represent the direction of incident radio frequency (RF) signals and the dynamic channel characteristics. A convolutional neural network (CNN) model is then trained with labeled samples of CSI phase difference. We implement a prototype of DeepAoA receiver using four synchronized USRPs with their antennas in uniform circular array (UCA) configuration for full field of view. We collect realworld CSI trace and conduct tracedriven simulations. DeepAoA can achieve AoA estimation errors of less than 3 degrees with a 98% confidence interval with four antennas. The results demonstrate the efficacy of DeepAoA.

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.