Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada.
Sensors (Basel). 2022 Feb 26;22(5):1858. doi: 10.3390/s22051858.
The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.
交通事故的数量不断增加，是当前交通系统中的一个重大问题。根据世界卫生组织（WHO）的数据，道路交通事故是全球第八大最高死因。超过 80%的道路交通事故是由分心驾驶引起的，例如使用手机、与乘客交谈和吸烟。为了解决驾驶员分心的问题，已经做出了很多努力；然而，并没有提供最佳的解决方案。解决这个问题的一个实际方法是实施驾驶员活动的定量措施，并设计一个可以检测分心行为的分类系统。在本文中，我们实现了一套经过验证的各种集成深度学习模型，这些模型可以有效地对驾驶员分心行为进行分类，并提供车内建议，以最大限度地减少分心程度，提高车内意识，从而提高安全性。本文提出了 E2DR，这是一种新的可扩展模型，它使用堆叠集成方法将两个或更多的深度学习模型结合起来，以提高准确性、增强泛化能力和减少过拟合，并提供实时建议。在应用于最先进的数据集时，包括 State Farm 分心驾驶员数据集，使用新颖的数据分割策略，包含 ResNet50 和 VGG16 模型的最高性能 E2DR 变体实现了 92%的测试准确性。
