Drowsy driving represents a major contributor to traffic accidents, and the
implementation of driver drowsy driving detection systems has been proven to
significantly reduce the occurrence of such accidents. Despite the development
of numerous drowsy driving detection algorithms, many of them impose specific
prerequisites such as the availability of complete facial images, optimal
lighting conditions, and the use of RGB images. In our study, we introduce a
novel approach called the Multi-Attention Fusion Drowsy Driving Detection Model
(MAF). MAF is aimed at significantly enhancing classification performance,
especially in scenarios involving partial facial occlusion and low lighting
conditions. It accomplishes this by capitalizing on the local feature
extraction capabilities provided by multi-attention fusion, thereby enhancing
the algorithm's overall robustness. To enhance our dataset, we collected
real-world data that includes both occluded and unoccluded faces captured under
nighttime and daytime lighting conditions. We conducted a comprehensive series
of experiments using both publicly available datasets and our self-built data.
The results of these experiments demonstrate that our proposed model achieves
an impressive driver drowsiness detection accuracy of 96.8%.

司机疲劳驾驶是交通事故的主要原因之一，通过引入多关注融合疲劳驾驶检测模型（MAF），可以显著提升分类性能，特别是在部分面部遮挡和低光照条件下，从而在提高算法整体稳健性方面发挥更大作用。通过在夜间和白天的光照条件下采集真实世界的数据集，我们进行了一系列综合实验，结果显示我们提出的模型达到了 96.8% 的司机疲劳检测准确率。

多关注融合的疲劳驾驶检测模型

Multi-Attention Fusion Drowsy Driving Detection Model

Drowsy driving has a crucial influence on driving safety, creating an urgent
demand for driver drowsiness detection. Electroencephalogram (EEG) signal can
accurately reflect the mental fatigue state and thus has been widely studied in
drowsiness monitoring. However, the raw EEG data is inherently noisy and
redundant, which is neglected by existing works that just use single-channel
EEG data or full-head channel EEG data for model training, resulting in limited
performance of driver drowsiness detection. In this paper, we are the first to
propose an Interpretability-guided Channel Selection (ICS) framework for the
driver drowsiness detection task. Specifically, we design a two-stage training
strategy to progressively select the key contributing channels with the
guidance of interpretability. We first train a teacher network in the first
stage using full-head channel EEG data. Then we apply the class activation
mapping (CAM) to the trained teacher model to highlight the high-contributing
EEG channels and further propose a channel voting scheme to select the top N
contributing EEG channels. Finally, we train a student network with the
selected channels of EEG data in the second stage for driver drowsiness
detection. Experiments are designed on a public dataset, and the results
demonstrate that our method is highly applicable and can significantly improve
the performance of cross-subject driver drowsiness detection.

本文首次提出了一种解释性引导通道选择（ICS）框架，通过设计一个两阶段的训练策略来逐步选择具有指导作用的关键通道，从而显着提高跨受试者司机嗜睡检测的性能。