# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### Dataset Generation  
A training dataset is needed to train a network that classifies driving behavior by inputting only 3-axis acceleration. Time series data are selected to explore temporal relationships in the input data. Furthermore, the training dataset needs labels that correspond to the time series inputs. Unfortunately, collecting data from only real vehicles with various driving behaviors to construct a dataset is difficult and risky. Therefore, we safely collect various driving data using the autonomous driving simulator IPG CarMaker together with driver data in real road environments to build a training dataset in various environments. Figure 9 is an example of CarMaker simulation for data set creation. Figure 9 is a map generated for data collection. This map was created to mimic the real road, DGIST campus. However, the actual driving lanes on the DGIST campus are one-lane or two-lane. To collect a lot of traffic and various behavioral data, the lanes were extended to three lanes in the simulation map. Furthermore, roundabouts were not created smoothly in the simulation, so we replaced the roundabouts with straight lanes. The driving distance of the vehicle in the campus driving simulation is 2.5 km, and it consists of straight lines and curves. Because roundabout is not taken into account, the driver's behavior may differ from the actual driving behavior on the DGIST campus. To supplement this, highway and urban scenarios were added to collect various driver behaviors in straight and curve. Figure 9 is the highway driving environment. The highway driving section is about 5.5 km straight and includes merging and splitting sections. The highway consists of a three-lane road, reducing to two lanes at 2.79 km. After this, at 3.02 km, it expands back to three-lane. The composition of the urban road environment is shown in Figure 9. All roads in the urban environment consist of one lane, and there are straight lines and curves including uphill and downhill. The total distance of urban road using dataset is approximately 4.15 km. Table 1 shows the configuration and speed limit of the simulated road for dataset generation. The speed limit in the campus road is set at 30 km/h. The speed limit in highway conditions is 100 km/h. Urban roads have various speed limits set according to the driving distance. To collect the driving behavior dataset through simulation, we built a simulation driving environment as shown in Figure 10. To mimic the real driving environment, we utilized a driving simulator. The compact driving simulator is composed of real vehicle parts, and the steering reaction force that feels the same as the real one is reproduced. We connected the driving simulator and CarMaker to perform simulation driving as shown in the Figure 10. Each simulated road environment has one driver-driven vehicle and more than 200 traffic vehicles that mimic real drivers. In this environment, the simulated driving was repeated 3 times for a total of 3 driving behaviors by 3 drivers along each road environment to generate 27 driving profiles, which were used as datasets. At each iteration, the location of the lane change event of all traffic vehicles except the control vehicle, the number of lane changes, and the initial target speed is randomly reassigned. Each traffic vehicle uses Adaptive Cruise Control (ACC) to avoid collisions with other vehicles. Thus, interaction with surrounding vehicles occurs. For example, in Figure 10, _Vehicle B_ decelerates rapidly under the influence of _Vehicle A_ making a lane change. The  
Figure 9: CarMaker simulation roads for dataset generation: (**a**) DGIST Campus environment. (**b**) Highway environment. (**c**) Urban environment. target speed of the traffic vehicle changes to a random value in the range of 10% of the speed limit every 1.5 s. In addition, each driving vehicle performed a lane change event in a random section, creating a complex environment similar to real roads. Driving datasets in real road environments were collected from real DGIST campus.