# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 2 Related Works  
Effective and reliable driving behavior analysis and early warning systems are required for the purpose of reducing traffic accidents and improving road safety [23]. For the classification of driving behavior, techniques using an in-vehicle camera [24; 25], and sensors that measure the movement of the vehicle [26; 27; 28] have been proposed. A technique using an in-vehicle camera detects unsafe driving by observing movement of the driver's eyes. Unsafe driving includes driver distraction, driver fatigue, and drowsy driving. The Inertial Measurement Units (IMU) sensor has been used in a number of studies to greatly improve the detection accuracy of driving events [10]. In conventional approaches, data from the IMU are used to detect the vehicle's driving behaviors through algorithms such as pattern matching, principal component analysis (PCA), and dynamic time warping (DTW). A MIROAD system that detects and recognizes driving events by fusion of the accelerometer, gyroscope, magnetometer, GPS, and the video sensor of a smartphone has been proposed [27]. The MIROAD system uses a rear camera, accelerometer, gyroscope, and GPS to classify only aggressive and non-aggressive behaviors based on multi-sensor data fusion and DTW algorithms. Standard driving events were collected from normal city driving, and aggressive driving events were collected in a controlled environment for safety. Aggressive driving was set with a hard left and right turns, swerves, and sudden braking and acceleration patterns. MIROAD showed an aggressive event detection performance of ~97% by combining the smartphone's gyroscope value, accelerometer value, and Euler angle rotation value. Conventional approaches using camera sensors may have problems with recognition performance due to environmental changes such as weather and illuminance [29]. In addition, some of the previous studies do not reflect various road environments or have not been verified in actual vehicles [30]. Specifically, aggressive driving on a curve is a factor that causes a crash accident, so it is necessary to predict driving behavior on a curve [10]. Additionally, the conventional method of detecting abnormal driver behavior by capturing and analyzing the driver's face and vehicle dynamics through image and video processing cannot capture the complex temporal features of driving behavior [31]. Recently, a technique for predicting driving maneuver through input of vehicle speed, acceleration, and steering wheel angle using deep learning has been proposed [32]. As the driving behavior of a vehicle can change with time, it is advantageous to use time series information rather than instantaneous information. Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and the Gated Recurrent Units (GRU), which are deep learning networks that utilize time series data for driver behavior profiling, have been used. For the comparison of the aggressive behavior classification performance of RNN, LSTM, and GRU, an evaluation based on data collected from a smartphone accelerometer has been proposed [33]. The proposed evaluation method constructed a dataset with 69 samples by driving a car on four different routes, and then classified seven types of driving events through RNN, LSTM and GRU. GRU and LSTM showed a classification accuracy of ~95% or better, whereas RNN was evaluated with a classification accuracy of ~70%. A driver behavior classification approach through CNN alone has also been proposed [34]. Information from the smartphone's accelerometer, gyroscope, and Engine Control Unit (ECU) was collected to distinguish driver behavior. The ECU information used is the vehicle's RPM, speed, and throttle, and is collected from an On-Board Diagnostic (OBD II) adapter. After that, a time-window applies to the nine signals collected to transform the temporal dependence of the signals into spatial dependence, and an image is generated through the recurrence plot technique. The generated image is trained by CNN, and has its own performance evaluation result of up to 99.99%.