# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 3 Research Methodology  
Figure 1 represents the overall framework of this research, which is structured as follows. Initially, criteria for aggressive driving were established through surveys since determining stages of aggressive driving can be subjective. To collect data on these stages, a survey was conducted with 100 participants using videos depicting various levels of aggressive driving scenarios simulated in a virtual simulator. Subsequently, the collected data were employed to develop an aggressive driving detection model. Detailed information regarding data preprocessing for aggressive driving detection and the developed model will be elucidated in the subsequent sections. Finally, the model's performance is evaluated through practical case studies.  
In this study, the detection criteria for aggressive driving on two-wheeled vehicles were narrowed down to rapid turn and rapid lane change. Rapid turn was considered suitable for aggressive driving detection, as it accounted for more than half of all two-wheeled vehicle accidents in Seoul, South Korea, in 2021 (KoROAD, [[https://taas.koroad.or.kr](https://taas.koroad.or.kr)]([https://taas.koroad.or.kr](https://taas.koroad.or.kr)), accessed on 1 June 2023). Rapid lane change was considered in comparison to rapid turn due to their limited distinction based on the time scale in Figure 2. Figure 2 illustrates the temporal changes in the z-axis gyroscope values for rapid turn and rapid lane change. As evident in the figure, distinct differences in the movement patterns of rapid turns and rapid lane change can be observed. In the case of rapid turns (Figures 1 and 2a), the maximal change in values occurs within 1 s, while for rapid lane change (Figure 2b), it is within 0.5 s. In addition, the maximum values of the z-axis angular velocity clearly differentiate between rapid turns and rapid lane changes. Furthermore, drivers change lanes to overtake other vehicles, whereas rapid lane changes are regarded as aggressive driving behaviors that can lead to accidents, such as rear-end collisions. Therefore, this study determined that rapid lane changes are also suitable for the category of aggressive driving detection on two-wheeled vehicles. Meanwhile, we chose to focus on items related to turns and thus excluded various aggressive driving behaviors, such as rapid acceleration, rapid deceleration, and close proximity to pedestrians.  
To detect aggressive driving, sensor data extracted from the Carla simulator were chosen with considerations for the feasibility of onboard installation on real two-wheeled vehicles. Image data and information about surrounding vehicles that can be extracted from camera and lidar sensors were excluded from consideration. Instead, acceleration, angular velocity, and position information were utilized.  
Figure 1: Research framework illustrating the approach to the aggressive driving detection model.  
Figure 2: z-axis gyroscope signals used for rapid turn detection (**a**) and rapid lane-change detection (**b**). The data are extracted from survey responses.  
The Carla simulator is an open-source simulator designed for autonomous driving research [15]. Apart from its application in autonomous driving research, it serves as a valuable platform for generating and modeling trajectory data for two-wheeled vehicles. The simulator recreates various environments and road conditions virtually to simulate the behavior of two-wheeled vehicles. It uses sensors like speed, gyroscope, and acceleration to generate data that resemble real-world driving situations. In this study, various aggressive driving scenarios and trajectory data that would be difficult to collect on actual roads were generated and analyzed using the simulator.