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.

Traffic light recognition is crucial for autonomous driving. While significant progress has been made in favorable conditions, recognition performance in night-time scenes remains a challenge. One straightforward approach is to apply enhancement methods that improve degraded images prior to object detection. However, since most enhancement methods are tailored for human perception; they may not consistently improve recognition accuracy for machine learning techniques. To address this, we propose an enhancement-guided framework for night-time traffic light recognition, called EG-TLR. EG-TLR consists of a residual denoising module (RDM) and a mixed attention traffic light detection module (MATLDM). The RDM reduces noise in degraded night-time images while preserving essential traffic light features by extracting sparsity information and performing context aggregation. The MATLDM improves feature extraction and recognition performance in complex night-time scenes by incorporating a shadow detection layer (SDL) and a mixed attention module (MAM). Moreover, to address the lack of a dedicated night-time traffic light dataset, we construct the Night-TL dataset utilizing publicly available images. Extensive experiments on Night-TL and LISA datasets demonstrate that EG-TLR achieves an AP50 of 79.83% and an AP50:95 of 36.62%, with an inference speed of 5.9 ms and 16.9 GFLOPs, outperforming other state-of-the-art methods. Furthermore, ablation studies and visualization results validate the effectiveness of our proposed method. The Night-TL dataset can be downloaded from: https://github.com/feiqinaqian/Night-TL-dataset.

The low-density parity-check (LDPC) decoding problem can be expressed as an integer linear programming (ILP) problem. One efficient method to solve the ILP problem is to relax the integer constraints and add penalty terms to the objective function, and the revised problem can be solved via the alternating direction method of multipliers (ADMM) algorithm. These penalty terms can punish the non-integral solutions and improve the decoding performance of the decoder. However, ADMM decoders are easily trapped in a local solution, which limits the frame error rate (FER) performance of the decoders at low signal-to-noise ratios (SNR). In this paper, we propose a restartable ADMM-based decoder using a negative proximal regularization. The negative proximal term will be updated whenever the decoder finds a new local solution. Therefore, the decoder can be restarted several times and the candidate solution which satisfies the parity-check equations and has the lowest objective function value can be selected as the decoder’s output. Some properties, together with several choices of penalty terms are discussed. We also investigate the convergence of our proposed decoder, and prove that the possibility of decoding errors is independent of the codeword that is transmitted. Simulation results show that our proposed decoder outperforms other ADMM-based decoders in most cases, while the decoding complexity maintains the same.