Currently, the number of traffic accidents has been increased globally. One of the main reasons for this increase is the distraction of the driver on the road. Distracted driving can cause collisions and cause injury, death, or property damage. New techniques can help to mitigate this problem, and one of the recent approaches is to employ body wearable sensors or camera sensors in the vehicle for real-time monitoring and detection of drivers’ distraction and behaviors, such as cell phone use, talking, eating, drinking, radio tuning, navigation interaction, or even combing hair while driving. However, this type of approach requires not only a powerful training module but also a lightweight module for real-time detection and analyzing the captured data. Data need to be collected from specific wearable or camera sensors in order to detect drivers’ distraction and ensure immediate feedback by the administrator for safe driving. Therefore, in this paper, we propose an effective camera-based framework for real-time identification of drivers’ distraction by using a deep learning approach with edge and cloud computing technologies. More specifically, the framework consists of three modules, including the distraction detection module deployed on edge devices in the vehicle environment, the training module deployed in the cloud environment, and finally the analyzing module implemented in the monitoring environment (administrator side) connected with a telecommunication network. The proposed framework is developed using two deep learning models. The first is a custom deep convolutional neural network (CDCNN) model, and the second one is a visual geometry group-16 (VGG16)-based fine-tuned model. Several experiments are conducted on a public large-scale driver distraction dataset to evaluate the two models. The experimental results show that the accuracy rates were 99.64% for the first model and 99.73% for the second model using a holdout test set of 10%. In addition, the first and second models have achieved accuracy rates of 99.36% and 99.57% using a holdout test set of 30%. The results confirmed the applicability and appropriateness of the adopted deep learning models for designing the proposed driver distraction detection framework.

当前，全球交通事故的数量已经增加。这种增加的主要原因之一是驾驶员在道路上的分心。分心驾驶可能会导致碰撞，并造成人身伤害，死亡或财产损失。新技术可以帮助缓解这一问题，最近的方法之一是在车辆中采用可穿戴式传感器或摄像头传感器，以实时监控和检测驾驶员的分心和行为，例如使用手机，说话，进食，喝酒，收音机调音，导航互动，甚至在开车时梳理头发。但是，这种方法不仅需要功能强大的训练模块，还需要轻量级的模块，用于实时检测和分析捕获的数据。需要从特定的可穿戴或相机传感器收集数据，以检测驾驶员的注意力并确保管理员立即反馈以安全驾驶。因此，在本文中，我们提出了一种有效的基于相机的框架，该框架通过使用具有边缘和云计算技术的深度学习方法来实时识别驾驶员的注意力。更具体地说，该框架由三个模块组成，包括在车辆环境中部署在边缘设备上的干扰检测模块，在云环境中部署的培训模块，最后是在监视环境（管理员侧）中实现的分析模块，该模块与电信网络。所提出的框架是使用两个深度学习模型开发的。第一个是自定义深度卷积神经网络（CDCNN）模型，第二个是基于视觉几何组16（VGG16）的微调模型。在大型公共驾驶员注意力分散数据集上进行了一些实验，以评估这两种模型。实验结果表明，使用10％的保持测试集，第一个模型的准确率为99.64％，第二个模型的准确率为99.73％。此外，使用30％的保持测试集，第一和第二个模型的准确率达到了99.36％和99.57％。结果证实了所采用的深度学习模型在设计拟议的驾驶员注意力分散检测框架方面的适用性和适当性。在大型公共驾驶员注意力分散数据集上进行了一些实验，以评估这两种模型。实验结果表明，使用10％的保持测试集，第一个模型的准确率为99.64％，第二个模型的准确率为99.73％。此外，使用30％的保持测试集，第一和第二个模型的准确率达到了99.36％和99.57％。结果证实了所采用的深度学习模型在设计拟议的驾驶员注意力分散检测框架方面的适用性和适当性。在大型公共驾驶员注意力分散数据集上进行了一些实验，以评估这两种模型。实验结果表明，使用10％的保持测试集，第一个模型的准确率为99.64％，第二个模型的准确率为99.73％。此外，使用30％的保持测试集，第一和第二个模型的准确率达到了99.36％和99.57％。结果证实了所采用的深度学习模型在设计拟议的驾驶员注意力分散检测框架方面的适用性和适当性。57％使用30％的保留测试集。结果证实了所采用的深度学习模型在设计拟议的驾驶员注意力分散检测框架方面的适用性和适当性。57％使用30％的保留测试集。结果证实了所采用的深度学习模型在设计拟议的驾驶员注意力分散检测框架方面的适用性和适当性。