# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### Base Network for Driving Behavior Classification  
Deep learning-based techniques are being studied to address various classification problems. Here, we describe state-of-the-art behavioral classification networks including CNN, LSTM, and CNN-LSTM models. Through each network analysis, the base network of our proposed system is selected and extended to improve performance. Figure 3 is a 1D-CNN network architecture. 1D-CNN is mainly used for classification using timeseries data with LSTM. Representative applications using 1D-CNN include real-time heart monitoring [39] and abnormality detection for EEG data [40], or classification of movements such as running and walking from an accelerometer data in HAR researches [41]. 1D-CNN and 2D-CNN consist of a convolutional layer, a pooling layer, an activation function, and a fully connected layer. 1D-CNN consists of two 1D convolutional layers and a fully connected layer. In addition, overfitting was prevented and computation was reduced through two maxpooling layers and one dropout. The final result is output as one of defensive, normal, and aggressive behaviors through softmax. 2D-CNN is generally applied to various classification techniques based on image input. Recently, a 2D-CNN technique for detecting driver behavior has been proposed [34]. The 2D-CNN network [34] uses a total of 9 signal data: 3-axis acceleration, 3-axis gyroscope, RPM, speed, and throttle information.  
Figure 2: LSTM structure.  
The 9 signal data are converted into 3 images with grayscale size of 150 \(\times\) 50 using the recurrence plot function of python. Each of the three images corresponds to an RGB image channel, and the final network input is an RGB image of size 3 \(\times\) 150 \(\times\) 50. However, as we only use 3-axis acceleration information, we create 50 \(\times\) 50 images with x-, y-, and z-axis accelerations as red, green, and blue channels of RGB image, respectively. The 2D-CNN network consists of a total of three convolutional layers and two fully connected layers, as shown in the Figure 4. In addition, the computation was reduced and overfitting was prevented using two dropouts and maxpooling, and one global maxpooling.  
The configuration of the LSTM network is as shown in Figure 5. The LSTM network consists of one LSTM layer and one fully connected layer. Overfitting is prevented through a dropout layer, and driving behavior is finally classified through a softmax.  
The CNN-LSTMs have been applied as an approach to predict text descriptions using image sequences. The CNN-LSTM architecture uses a CNN layer in the feature extraction process of the input data integrated with LSTM to support sequence prediction, as shown in Figure 6. The structure of the network consists of two convolutional layers: one LSTM layer  
Figure 4: 2-Dimensional Convolution Neural Network (2D-CNN) structure [34].  
Figure 5: LSTM structure [37].  
Figure 3: 1-Dimensional Convolution Neural Network (1D-CNN) structure [39].  
and one fully connected layer. In addition, overfitting is prevented with one maxpooling and two dropouts.  
A 4-layer CNN-LSTM network, a state-of-the-art method for classifying human activity, is presented in Figure 7 [35]. The 4-layer CNN-LSTM was applied to the HAR research area by inputting the smartphone's 3-axis acceleration and 3-axis gyroscope information. The basic structure of the 4-layer CNN-LSTM is similar to the CNN-LSTM architecture, and there are four convolutional layers before the LSTM layer. Then, a max-pooling layer is added to summarize the feature map and reduce computational cost. Furthermore, two dropouts are inserted to prevent overfitting. Unlike 2D-CNN, which converts signal data into an image and uses it as an input, 4-layer CNN-LSTM uses signal data directly as an input to the network. In addition, the structure combining 1D-CNN and LSTM has strengths in feature extraction of input data and time series prediction performance, so it is selected as a base model for behavior classification.