# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 1 Introduction  
In the past decade, remarkable advances have been realized in various fields such as object detection [1, 2], tracking [3, 4], control [5], and Vehicle-to-Everything (V2X) communication [6, 7] to achieve the goal of autonomous driving. Object detection uses sensors such as cameras, lidar, and radar to detect objects that affect driving. Intelligent driving assistance systems are being proposed through object tracking and control algorithms based on detected objects. Driving applications including adaptive cruise control (ACC) that assists driving is a representative example [8, 9]. V2X provides not only vehicle-to-vehicle, but also vehicle-to-infrastructure connectivity, providing safety applications such as construction section warnings, stop sign violations and curve speed warnings [10]. These driving applications and safety applications have helped reduce the stress on many drivers [11, 12].  
Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system [13, 14]. According to the World Health Organization, statistics are reported that ~1.3 million people die in traffic accidents each year [15]. In the US alone, an average of 6 million traffic accidents occurs each year [16],and a study has reported that aggressive driving is more than 56% of the causes of traffic accidents [17]. Aggressive driving behavior can cause traffic accidents or adversely affect the driving of adjacent vehicles, resulting in traffic congestion [18; 19]. Advances in communication, intelligent transportation systems and computer systems provide opportunities to provide traffic safety, convenience, and efficiency. Along with deep learning, the V2X system is an effective approach that can collect information from a variety of sources, broaden driver awareness and prevent potential accidents [20]. V2X communication is one of the key elements to increase both the safety and efficiency of driving vehicles by expanding the vehicle network, which aims to increase the safety and efficiency of the transportation system by sharing information between vehicles, pedestrians, and infrastructure.  
We aim to improve traffic safety by predicting aggressive driving behavior using only low-cost sensor data and sharing it with the surrounding vehicles to reduce the cycle of adverse impacts. Therefore, we propose a 4-layer CNN-2 stacked LSTM-based approach for aggressive behavior classification and a V2X-based driving behavior sharing system. The proposed system reduces cost by using time series data of only 3-axis accelerations to classify aggressive behavior. In addition, we use the traffic simulator IPG CarMaker [21] to build a road that mimics the real-world environments, and generate a training dataset by repeatedly human driving vehicles with various driving behaviors. The training dataset is supplemented by adding real road driving data as well as simulators. The vehicle's driving behavior is shared with nearby vehicles [22], pedestrians or traffic light systems through V2X communication. We built a prototype module including an NVIDIA Jetson TX2 board and a V2X device. Vehicles equipped with the prototype module transmit driving behavior in real time to the surrounding vehicles via V2X communication. The proposed system has been verified in the simulation and a real vehicle, and can be configured at low cost with only an IMU, V2X device, and a mini PC. In addition, traffic safety can be improved by notifying the surrounding vehicles of aggressive driving behavior, and there is a possibility of applying various applications through V2X communication.  
The structure of this paper is as follows. In Section 2, the conventional researches to detect driving behaviors are discussed. A deep learning-based driving behavior detection and V2X system are described in Section 3. The proposed system has been validated in Section 4, and the remaining challenges and conclusions are discussed in Section 5.