A 20% rise in car crashes in 2021 compared to 2020 has been observed as a
result of increased distraction and drowsiness. Drowsy and distracted driving
are the cause of 45% of all car crashes. As a means to decrease drowsy and
distracted driving, detection methods using computer vision can be designed to
be low-cost, accurate, and minimally invasive. This work investigated the use
of the vision transformer to outperform state-of-the-art accuracy from 3D-CNNs.
Two separate transformers were trained for drowsiness and distractedness. The
drowsy video transformer model was trained on the National Tsing-Hua University
Drowsy Driving Dataset (NTHU-DDD) with a Video Swin Transformer model for 10
epochs on two classes -- drowsy and non-drowsy simulated over 10.5 hours. The
distracted video transformer was trained on the Driver Monitoring Dataset (DMD)
with Video Swin Transformer for 50 epochs over 9 distraction-related classes.
The accuracy of the drowsiness model reached 44% and a high loss value on the
test set, indicating overfitting and poor model performance. Overfitting
indicates limited training data and applied model architecture lacked
quantifiable parameters to learn. The distracted model outperformed
state-of-the-art models on DMD reaching 97.5%, indicating that with sufficient
data and a strong architecture, transformers are suitable for unfit driving
detection. Future research should use newer and stronger models such as
TokenLearner to achieve higher accuracy and efficiency, merge existing datasets
to expand to detecting drunk driving and road rage to create a comprehensive
solution to prevent traffic crashes, and deploying a functioning prototype to
revolutionize the automotive safety industry.

2021 年与 2020 年相比，车辆事故率上升 20％，其中 45％的车祸是由于疲劳和分心驾驶所致。这项研究使用计算机视觉设计了低成本、精度高且侵入性小的检测方法，探讨了使用视觉变换器来优于 3D-CNNs 的最新准确性，针对分心和疲劳驾驶分别训练了两个变压器。其中，分心的模型表现超越最先进的模型，达到 97.5％的准确度。建议未来的研究使用新的和更强大的模型来实现更高的准确性和效率，在现有数据集的基础上扩展到检测醉酒驾驶和道路愤怒，以创建全面的解决方案来防止交通事故。

运用时空关注机制与视觉变换器识别分心和疲劳驾驶

Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers

Drowsiness can put lives of many drivers and workers in danger. It is
important to design practical and easy-to-deploy real-world systems to detect
the onset of drowsiness.In this paper, we address early drowsiness detection,
which can provide early alerts and offer subjects ample time to react. We
present a large and public real-life dataset of 60 subjects, with video
segments labeled as alert, low vigilant, or drowsy. This dataset consists of
around 30 hours of video, with contents ranging from subtle signs of drowsiness
to more obvious ones. We also benchmark a temporal model for our dataset, which
has low computational and storage demands. The core of our proposed method is a
Hierarchical Multiscale Long Short-Term Memory (HM-LSTM) network, that is fed
by detected blink features in sequence. Our experiments demonstrate the
relationship between the sequential blink features and drowsiness. In the
experimental results, our baseline method produces higher accuracy than human
judgment.

本文提出用于早期疲劳检测的多层次多尺度长短时记忆网络 (HM-LSTM)，结合测量到的眨眼特征，对大型真实数据集进行了测试，发现基于眼睑特征的检测方法比人类判断更准确。