MADFM: Multi-Layer Adaptive Driver Fatigue Monitoring Model Based on Steering Wheel Signals

Driver fatigue poses a serious threat to road safety. Although most vehicles are equipped with driver fatigue monitoring models, the performance of existing models is not satisfactory due to the complexity and unclear reliability of fatigue features. To address these issues, this study proposed a multi-layer adaptive driver fatigue monitoring (MADFM) model, based on the steering wheel signals obtained from 25 participants in a driving simulator. The steering wheel signals were sequentially processed through an adaptive window partitioning algorithm based on sample entropy, feature extraction, and an adaptive outlier point removal algorithm based on local outlier factor. Finally, a multi-scale adaptive feature selection (AFS) algorithm based on global-local correlation was applied to obtain the optimal feature set ${D} _{o}$ . AFS algorithm was then validated using a random forest model and compared with existing feature selection algorithms. The results showed that after adopting the AFS algorithm, the model’s fatigue monitoring accuracy reached 93.71%, which was 10.21% higher than the algorithm without using feature selection. Compared to four common feature selection algorithms, AFS algorithm demonstrated superior performance in terms of monitoring accuracy, precision, recall and interpretability. The interpretability analysis results revealed that steering wheel angle and lateral acceleration features were the most sensitive features affecting the accuracy of the fatigue monitoring model. This model could improve the monitoring performance of driver fatigue, prompting safer driving behaviors, and reducing traffic accidents risk.