Detecting abnormal driving behavior is critical for road traffic safety and
the evaluation of drivers' behavior. With the advancement of machine learning
(ML) algorithms and the accumulation of naturalistic driving data, many ML
models have been adopted for abnormal driving behavior detection. Most existing
ML-based detectors rely on (fully) supervised ML methods, which require
substantial labeled data. However, ground truth labels are not always available
in the real world, and labeling large amounts of data is tedious. Thus, there
is a need to explore unsupervised or semi-supervised methods to make the
anomaly detection process more feasible and efficient. To fill this research
gap, this study analyzes large-scale real-world data revealing several abnormal
driving behaviors (e.g., sudden acceleration, rapid lane-changing) and develops
a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method
using partly labeled data to accurately detect the identified abnormal driving
behaviors. Moreover, previous ML-based approaches predominantly utilize basic
vehicle motion features (such as velocity and acceleration) to label and detect
abnormal driving behaviors, while this study seeks to introduce Surrogate
Safety Measures (SSMs) as the input features for ML models to improve the
detection performance. Results from extensive experiments demonstrate the
effectiveness of the proposed semi-supervised ML model with the introduced SSMs
serving as important features. The proposed semi-supervised ML method
outperforms other baseline semi-supervised or unsupervised methods regarding
various metrics, e.g., delivering the best accuracy at 99.58% and the best F-1
measure at 0.9913. The ablation study further highlights the significance of
SSMs for advancing detection performance.

利用 Hierarchical Extreme Learning Machines 和 Surrogate Safety Measures 开发了半监督机器学习方法，准确检测出鉴定的异常驾驶行为，通过广泛实验验证了该方法在各种指标上的有效性。