Convolutional neural networks for 5Genabled Intelligent Transportation System : A systematic review Abstract   Modern 5Genabled Intelligent Transportation System (ITS) provides comfort and safety to the end users by using various models and techniques most of which are based on the machine learningbased techniques. However, a large number of issues are there such as congestion control, safety and security, traffic management in modern ITS for which AIbased techniques can be used. From the existing literature, it has been observed that, deep learning based techniques are widely used for object detection and classification which in turns can provide unforgettable experience to both the users and drivers. Specifically, Convolutional Neural Networks (CNN) based techniques are widely used for object detection and classification in modern ITS. Motivated from these facts, in this paper, we review different types of CNN models used in Intelligent Transportation System for traffic sign recognition, traffic light detection, vehicle classification and pedestrian detection and many more. Various CNN based models and techniques are discussed and compared in comparison to the existing schemes using various performance evaluation metrics. From the indepth survey given in this paper, users can identify that which particular model can be used in modern ITS with respect to its merits over the others.

Pose Recognition for Dense Vehicles under Complex Street Scenario Locating vehicles in the surrounding environment and distinguishing their poses are crucial in autonomous driving systems. An intelligent transportation system expects a visionbased object detector not only to detect and classify vehicles but also to recognize the poses of them in estimating their future movement direction. However, most of the current object detection algorithms only classify different classes of vehicles; and there are rare studies on classifying different poses within the same class of vehicles. In addition, the currently available datasets have no annotation data that are specific to different poses of intraclass vehicles. To achieve the vehicles pose recognition, we build a vehicle pose dataset based on the Cityscapes dataset. Furthermore, we also improve the network model of the YOLO v2 detector to support simultaneous vehicle detection and pose classification with an improvement of 10.71% mean average precision (mAP) comparing to the default network which runs in realtime with 29 FPS.