A Comparative Study on Unsupervised Machine Learning Models for Detecting Sudden Lane Changes

Abstract

Lane-changing behaviour detection is a critical aspect of driving safety and traffic management. This study focuses on detecting sudden lane changes as a subset of abnormal driving behaviours. By analyzing the characteristics of abrupt lane changes, the aim is to develop effective data-driven unsupervised machine learning (ML) methods for their detection and classification. Three unsupervised ML models, namely Isolation Forest, Local Outlier Factor, and Robust Covariance are evaluated and compared using a dataset of lane-change events. The results show that the Isolation Forest and Local Outlier Factor models outperform the Robust Covariance model, with the Local Outlier Factor model excelling in precision and overall accuracy, achieving the best overall detection rate. Both Robust Covariance and Isolation Forest deliver satisfactory results. Conversely, the Robust Covariance model exhibits poor performance. The findings verify the capability of data-driven ML methods for enhancing road safety and driving experiences through effective detection of sudden lane changes using vehicle motion information data. Future work involves further improving the accuracy and reliability of the ML models, validating their generalizability on larger datasets, incorporating contextual information, and exploring their real-time implementation in driving assistance systems.