Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection

arXiv:2312.04610v2 Announce Type: replace
Abstract: 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 …

abstract advancement algorithms arxiv behavior behavior detection cs.ai cs.lg data data-driven detection drivers driving driving data eess.sp evaluation machine machine learning ml models safety safety measures semi-supervised stat.ot supervised machine learning traffic traffic safety type