This study presents a comparative analysis of machine learning models used for driver classification based on microelectromechanical system (MEMS) sensor data. The research utilizes the “UAH-DriveSet” open dataset, which includes over 500 minutes of driving data with annotations for aggressive driving events, such as sudden braking, sharp turns, and rapid acceleration. The models evaluated in this study include gradient boosting algorithms, a recurrent neural network and a convolutional neural network. Special attention is given to the impact of data segmentation parameters, specifically window size and overlap, on classification performance using the sliding window method. The effectiveness of each model was assessed based on classification metrics such as accuracy, precision, and F1 score. The results show that gradient boosting “LightGBM” outperforms the other models in terms of accuracy and F1 score, while long short-term memory model demonstrates good performance with time-series data but requires larger datasets for better generalization. Convolutional neural network, while effective for identifying short-term patterns, faced difficulties with class imbalances. This research provides valuable insights into selecting the most appropriate machine learning models for driver behavior classification and offers directions for future work in developing intelligent systems using MEMS sensor data.

Keywords: driver behavior analysis, microelectromechanical system sensors, machine learning, aggressive driving, gradient boosting, recurrent neural networks, convolutional neural networks, sliding window, driver classification

Detecting aggressive and abnormal driver behavior, which depends on a multitude of external and internal factors, is critically important for enhancing road safety. This article provides a comprehensive review of machine learning methods applied for driver behavior classification. An extensive analysis is conducted to assess the pros and cons of existing machine learning algorithms. Various approaches to problem formulation and solution are discussed, including supervised and unsupervised learning techniques. Furthermore, the review examines the diverse range of data sources utilized in driver behavior classification and the corresponding technical tools employed for data collection and processing. Special emphasis is placed on the analysis of Microelectromechanical Systems sensors and their significant contribution to the accuracy and effectiveness of driver behavior classification models. By synthesizing existing research, this review not only presents the current state of the field but also identifies potential directions for future research, aiming to advance the development of more robust and accurate driver behavior classification systems.

Keywords: machine learning, driver classification, driver behavior, data source, microelectromechanical system, driver monitoring, driving style, behavior analysis