# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 2 Related Works
Despite the high classification performance of the introduced method, it requires a prior work to convert the signal into an image. The deep learning method using the accelerometer of a smartphone is being applied to Human Activity Recognition (HAR) in addition to driver behavior classification [35,36]. In the state-of-the-art approaches, a combined network of CNN-LSTM is applied rather than one kind of network of CNN or LSTM. The CNN-LSTM network extracts the features of the input signal through the CNN and uses it as the input of the LSTM. The combination of CNN and LSTM improves recognition performance by utilizing both spatial and temporal information. A 4-layer CNN-LSTM network has been proposed as the latest approach to improve the recognition performance in CNN-LSTM networks [35]. The 4-layer CNN-LSTM is a CNN-LSTM network with four one-dimensional convolutional layers and a maxpooling layer to summarize the feature map and reduce the computational cost. The 4-layer CNN-LSTM network showed about 99.39% of classification performance in HAR. To classify aggressive driving behaviors using deep learning, a dataset for training a neural network is required. However, the actual road environment is very wide and the behavior of drivers is very diverse, so it is difficult to construct the dataset. For the generation of datasets for neural network training, simulators are an efficient alternative. IPG's TruckMaker-based driver model was used to train LSTM to classify the truck driver model [37]. The proposed technique categorizes the truck driver model into six types based on longitudinal and lateral acceleration, engine speed, vehicle speed, throttle, and pitch angle. TruckMaker was used to create a vehicle model with five different trailer loads of 0, 5, 10, 15, and 20 tones for the LSTM's training data generation. As a result, it classifies drivers by approximately 82% and 74% classification accuracy on the training and test set. However, the training model of this method has not been validated in a real environment. In addition, the proposed method only performs the classification of the driver model and does not detect aggressive driving behaviors or driving events. The proposed system combines a classifier that classifies the driving behavior of individual vehicles as \(defensive\), \(normal\), or \(aggressive\), and a V2X system that shares driving behavior. The driving behavior classifier is extended based on a 4-layer CNN-LSTM network with high behavior classification performance without requiring pre-processing of input data such as signal to image converting. As the input of the proposed network uses only 3-axis acceleration representing vehicle movement, the system cost is low and it is robust in the environment changes. In addition, we present the comparison results of the classification performance of the proposed network and the networks presented in other literature. We include not only driving behavior classification, but also behavior classification results in Basic Safety Message (BSM) to form a prototype of a system that shares with surrounding vehicles through V2X communication. Finally, we contribute to improving driving safety by providing an example of an advanced ACC system through sharing driving behavior.