Code and data underlying the publication: Data-driven Semi-supervised Machine Learning with Safety Indicators for Abnormal Driving Behavior Detection

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This is the code and processed data related to the publication:Dong, Y., Zhang, L., Farah, H., Zgonnikov, A., & van Arem, B. (2023). Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection. arXiv preprint arXiv:2312.04610. https://arxiv.org/abs/2312.04610 The original data is from https://github.com/UCF-SST-Lab/UCF-SST-CitySim1-DatasetThe codes make use of open-sourced implementation of HELM and other semi-supervised learning algorithms. After setting up the folder and fetching the data, one can simply run the code with the specific function (identified by their names) get the relevant results.Details about the implementation are demonstrated in the paper. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Detecting abnormal driving behaviour is critical for road traffic safety and the evaluation of drivers' behaviour. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behaviour detection (also referred to as anomalies). Most existing ML-based detectors rely on supervised methods, which require substantial …

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