# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
###### Abstract  
The growing concerns over road safety and the increasing popularity of two-wheeled vehicles highlight the need to address aggressive driving behaviors in this context. Understanding and detecting such behaviors can significantly contribute to rider safety and accident prevention. The primary aim of this research is to develop an effective method for detecting aggressive driving patterns, specifically focusing on rapid turns and lane-change maneuvers using two-wheeled vehicles. To achieve this objective, we conducted a survey to establish criteria for aggressive driving. Subsequently, we collected data through a virtual simulator, implementing staged aggressive driving scenarios. The data underwent preprocessing, feature engineering, and deep learning model training for detection. The results of this study demonstrate the successful detection of aggressive driving patterns, including rapid turns and lane changes, using sensor data. The criterion for rapid turns is specified as a significant change in sensor values within 1 s. In the CNN-LSTM model for aggressive lane changes, the precision for normal driving is 0.97, and the overall accuracy for aggressive driving is 95%. Our approach, which relies on sensor technology rather than impractical camera systems, showcases the potential for enhancing rider safety in two-wheeled vehicles. In conclusion, this research provides valuable insights into the detection of aggressive driving patterns in two-wheeled vehicles. By leveraging sensor data and innovative methods, it offers promising implications for improving rider safety and accident prevention in the future. Keywords:aggressive driving patterns; two-wheeled vehicles; sensor-based approaches; Carla simulator; CNN-LSTM +
Footnote †: journal: Medical  
each violation are established through analysis of discomfort metrics of passengers using pupil reaction and electrocardiogram tests, and a comprehensive safety driving index is developed based on violation-specific weights (eTAS). In the case of data collection systems for commercial vehicles, real-time data such as GPS, speed, RPM, brake usage, and distance traveled are collected using a Digital Tachograph (DTG) to analyze vehicle operation patterns. However, the absence of data collection and analysis platforms for two-wheeled vehicles results in a significant lack of foundational data. Two-wheeled vehicles have greater maneuverability than four-wheeled vehicles, enabling more aggressive driving behaviors like rapid turns and lane changes. Due to these reasons, methods for detecting and evaluating aggressive driving behavior in two-wheeled vehicles are limited. As a result, studies on safe driving indices for two-wheeled vehicles have been conducted based on restricted assessment criteria, such as speed, acceleration, and work environment [2]. The detection methods for driving behaviors like rapid turns and rapid lane changes, which can lead to accidents, are also inadequate. Simulation technology and artificial intelligence (AI) based on data are necessary for developing methods to detect aggressive driving behaviors using two-wheeled vehicles. Due to the absence of a data collection system for two-wheeled vehicles, acquiring real-world data on aggressive driving behaviors with these vehicles is practically challenging. Therefore, there is a need to use simulation technology that can replicate real-world situations to acquire data. Additionally, considering the free-spirited driving behaviors of two-wheeled vehicles, AI technologies based on time-series data are essential for detecting aggressive driving. Time-series-based AI technologies can track driving characteristics such as speed, acceleration, and rotation over time [3]. These technologies can effectively distinguish between normal driving patterns and abnormal driving patterns associated with aggressive driving. They take advantage of the ability to differentiate between these patterns [4].