Vehicular Environment Identification Based on Channel State Information and Deep Learning

This paper presents a novel vehicular environment identification approach based on
deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of
Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the
environment type in which the vehicle is driving, without any need to implement specific sensors
such as cameras or radars. We consider environment identification as a classification problem, and
propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI
is used as the input feature to train the model. To perform the identification process, the model is
targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The
proposed model is extensively evaluated, showing that it can reliably recognize the surrounding
environment with high accuracy (96.48%). Our model is compared to related approaches and state-ofthe-art classification architectures. The experiments show that our proposed model yields favorable
performance compared to all other considered methods.