Self-Supervised Driver Distraction Detection for Imbalanced Datasets

The role of driver distraction in the occurrence of road traffic accidents is of pivotal importance on a global scale. Furthermore, the effective detection of distraction is complicated by the imbalanced nature of typical driving datasets, which often lack sufficient observations of certain behaviors. This study examines the task of driver distraction detection using the State Farm Distracted Driver Detection and the Drive&Act datasets. The primary objective is to address the issue of data imbalance in a label-free manner in an unlabeled training dataset. To address the issue of data imbalance, we propose a novel data-loading technique, Clustered Feature Weighting (CFW). This label-free approach integrates transfer learning, unsupervised clustering, and weighted random sampling. The proposed method improves class balance during model training, thereby enhancing overall performance and robustness. The methodology encompasses model selection and embedding extraction, variance analysis, clustering, and weight generation. Experiments are conducted using both supervised and self-supervised learning (SSL) methods. The results demonstrate the effectiveness of SSL methods in enhancing the adaptability and robustness of driver distraction detection systems, particularly in handling non-uniform class distributions. CFW significantly improves class balance within training batches, thereby enhancing the accuracy and generalization of driver distraction detection models. Our code will be published on GitHub.