School of Transportation, Southeast University, Nanjing 210018, China.
Sensors (Basel). 2022 Mar 19;22(6):2373. doi: 10.3390/s22062373.
Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver's distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver's distracted behavior recognition systems. The driver's posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver's real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver's microscopic behavior to establish a more comprehensive proactive surveillance system.
道路交通事故与商用车有关，被认为是限制社会经济稳定发展的一个重要因素，这与驾驶员的注意力分散行为密切相关。然而，现有的驾驶员注意力分散行为监测系统在监测和预防驾驶员注意力分散行为方面仍然存在一些缺点，例如识别对象和场景较少。本研究旨在提供一个更全面的方法框架，以展示扩大现有驾驶员注意力分散行为识别系统的识别对象、场景和类型的重要性。本研究首先分析了驾驶员的姿势特征，为后续的建模提供了基础。为了提高识别效率，针对不同的姿势类别建立了五个 CNN 子模型，并建立了一个整体的多级级联 CNN 框架。为了提出最佳模型，对包括 117410 张日间图像和 60480 张夜间图像在内的商用车驾驶员姿势图像数据集进行了训练和测试。研究结果表明，与非级联模型相比，日间和夜间级联模型的性能都更好。此外，对于非级联和级联模型，夜间模型的精度较差，速度较快。本研究可以用来制定提高驾驶员安全性的对策，并为驾驶员实时监控和预警系统以及自动驾驶系统的设计提供有用信息。未来的研究可以结合车辆状态参数和驾驶员的微观行为，建立一个更全面的主动监测系统。
