# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### Dataset Generation
Figure 11 is a vehicle used for real road dataset collection and algorithm testing. Real road driving data were collected from one driver repeating three driving with three driving behaviors under a speed limit of 30 km/h. Through the simulation and real-world driving, we obtained 3-axis acceleration information when passing a speed bump as well as going straight, curve, left turn, right turn, uphill, and downhill according to driving behaviors. In addition, real-world and simulation drivers themselves labeled driving aggression. As a driver's self-definition can be subjective, we followed the behavior of aggressive driving as defined by the AAA Foundation [44]. Among the aggressive driving behaviors, speeding in heavy traffic, cutting in front of another driver and then slowing down, and blocking cars attempting to pass or change lanes were performed. Defensive behavior was performed following the guidelines of the New York Department of Motor Vehicles (DMV) [45], such as maintain the correct speed, allow space. Normal behavior was determined to be between aggressive and defensive behavior. The driver performed sufficient practice driving for about 10 h or more according to the guidelines, and then data for each driving behavior were collected. All processes for dataset generation and labeling were performed under the supervision of a supervisor. The supervisor has a clear understanding of the guidelines for driving behavior and is a veteran driver with over 10 years of driving experience. If the driver selects the defensive behavior label but does not match the guidelines, such as exceeding the speed limit, data and labels are excluded. In addition, unrealistic driving data and intentional collision data with vehicles or pedestrians are also excluded. Table 2 represents the standard deviation and median for each collected driving behavior. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Environment Variable**} \\ \hline
**Types** & \begin{tabular}{c} **Total Road** \\ **Length** \\ \end{tabular} & **Road Section** & **Limit Speed** & **\# of Lanes** \\ \hline DGIST Campus & 2256.3 m & 0\(\sim\)2256.3 m & 30.0 km/h & 1 \\ \hline Highway & 5509.0 m & \
\begin{tabular}{c} 0\(\sim\)2790 m \\ 2790\(\sim\)3020 m \\ 3020\(\sim\)5509 m \\ \end{tabular} & 100.0 km/h & 3 \\ \hline Urban & 4154.5m &
\begin{tabular}{c} 0\(\sim\)280 m \\ 280\(\sim\)315 m \\ 315\(\sim\)1470 m \\ 1470\(\sim\)1530 m \\ 1530\(\sim\)1855 m \\ 1855\(\sim\)1930 \\ 1930\(\sim\)2175 m \\ 2175\(\sim\)4154.5 m \\ \end{tabular} & 100.0 km/h & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Predefined driving environment variables in CarMaker. Figure 10: CarMaker simulation environments for dataset generation: (**a**) Driving environment. (**b**) Simulation driving. (**c**) Simulation scenario. standard deviation of the x-axis acceleration represents an indicator for the acceleration and deceleration of the vehicle, and the larger the standard deviation, the larger the amount of change in acceleration and deceleration. The standard deviation of the y-axis acceleration represents an indicator of the vehicle's rotation, and the larger the standard deviation, the greater the amount of change in steering. As the z-axis acceleration is changed by the rolling and pitching of the vehicle, the standard deviation increases as the x-axis and y-axis accelerations change larger. Therefore, driving with aggressive behavior increases the standard deviation of the 3-axis acceleration. The median of the 3-axis acceleration for each driving behavior collected is close to zero, indicating that the collected data are not biased. We built a dataset from the collected 3-axis acceleration information and labels. The collected data are a total of 93,286 3-axis acceleration data and labels, consisting of 79,052 simulations and 14,234 real vehicle data. Defensive behavior is 32,650, normal behavior is 30,784, and aggressive behavior is 29,852, which are balanced in proportions of 35%, 33%, and 32% in the total dataset, respectively.