# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 2 Literature Review  
Aggressive vehicle driving has traditionally been assessed based on factors such as sudden acceleration, abrupt braking, rapid lane changes, and sharp turns. However, for two-wheeled vehicles, their relatively more agile and maneuverable nature, compared to conventional passenger cars and trucks, often leads to frequent safety accidents caused by excessive, rapid lane changes and turns [1]. In the context of rapid lane changes, research has predominantly utilized image-based algorithms to detect lane changes [5; 6; 7; 8; 9], often incorporating information about surrounding vehicles [3; 10; 11; 12; 13]. [PERSON] et al. (2020) employed the LaneNet model to detect lanes from black-box vehicle dashcam footage, determining lane change based on the distance from the lane's center [9]. [PERSON] et al. (2019) used RGB images from a vehicle's camera sensor in the Carla simulator to detect lane change [8]. [PERSON] et al. (2020) utilized the YOLOv3 model to detect leading vehicles in black-box video and identified rapid lane change through the zigzag pattern of detected object movements in video frames. However, applying the methods used in existing research for lane-change detection, which often relies on attaching image sensors for lane detection, is practically challenging for two-wheeled vehicles due to limitations in sensor placement. The distinctive free-form driving patterns of two-wheeled vehicles impose limitations on the extraction of rapid lane changes from video footage when equipping them with image sensors. In particular, individual lane-change detection using images from individual vehicle cameras presents constraints. [PERSON] et al. (2017) tackled this differently, detecting illegal lane changes from CCTV footage by tracking individual vehicle trajectories [5]. This approach focuses on utilizing movement characteristics and trajectories rather than solely relying on individual vehicle imagery for detection. [PERSON] et al. (2017) utilized the relative velocity between the target vehicle attempting to perform a lane change and its front and rear vehicles to distinguish between zigzag driving within the lane and an actual lane change [11]. They employed an SVM model for detection. Similarly, [PERSON] et al. (2019) used SVM and decision tree models with information about surrounding vehicles to detect lane change [10]. [PERSON] et al. (2022) applied the XGBoost model to predict lane change using driving trajectory data, incorporating various information such as angular velocity and distance from surrounding vehicles [3]. They emphasized utilizing information from the surrounding traffic flow for predicting lane change and improved model accuracy by adjusting the segment near the lane-change point. While traditional machine learning techniques can be used to detect lane change, it remains challenging to equip individual two-wheeled vehicles with devices capable of detecting information about surrounding vehicles. On the other hand, [PERSON] et al. (2022) estimated lane change using data on relative distances and driving speeds of adjacent vehicles [12]. They compared various machine learning models and a multilayer perceptron in terms of architectural complexity. Interestingly, even with a simpler architecture, like a perceptron layer, models incorporating deep learning outperformed traditional machine learning models. Furthermore, there have been attempts to detect rapid lane change using simple sensor data [14]. In their study, [PERSON] and [PERSON] (2019) classified vehicle behaviors, including lane changes, rotations, and U-turns, using only smartphone sensors such as accelerometers, magnetometers, and gyroscopes. The experiment involved 20 real-world drivers, which imposes limitations on applying complex techniques like artificial neural networks due to the relatively small dataset. While there were cases where lane changes were misclassified as rotations, the overall accuracy was 87%, indicating the potential for accuracy improvement with increased data availability.