交通运输研究2023，Vol.9Issue(6)：78-87,118,11.DOI:10.16503/j.cnki.2095-9931.2023.06.008
融合机器视觉与高精度定位的高速公路疲劳驾驶行为检测方法
Highway Fatigue Driving Detection Method Based on Machine Vision and High Precision Positioning
摘要
Abstract
In order to improve the accuracy of fatigue driving detection in highway scenes and reduce the misjudgment rate of fatigue driving detection in cockpit where driver's face is easily obscured and light is complicated,a highway fatigue driving behavior detection method combining detection accura-cy and real-time performance,high-precision positioning and machine vision was proposed.Firstly,multi-task convolutional neural network and FaceNet algorithm were used to recognize the driver iden-tity,and the improved PFLD(Practical Facial Landmark Detector)algorithm was used to detect face key points.Secondly,considering the disturbance caused by the unstable working state of video equip-ment to the recognition of fatigue driving behavior,the SLDF(Slope of Longitudinal Displacement Fluctuation)was proposed based on the track data of running vehicles,to make up for the single video equipment detection is susceptible to interference factors such as light,occlusion.Thirdly,SLDF index and 3 facial fatigue features were used to identify fatigue driving,and quantum particle swarm optimi-zation algorithm was added to traditional SVM(Support Vector Machines)to improve SVM classifica-tion accuracy and shorten operation time.Finally,in order to verify the performance of the model,a re-al car test was conducted,the results showed that the fatigue driving recognition accuracy was 86.8%in complex scenarios,and the calculation time was 3.017 s;compared with other existing data fusion algorithms,the classification accuracy and operation efficiency of the improved SVM were improved.It indicates that the fusion of trajectory,facial multi-dimension and multi-source information effective-ly improves the detection accuracy and robustness of the system in identifying fatigue driving,and can provide strong support for the subsequent detection of fatigue characteristics in highway scenarios.关键词
交通安全/人脸识别/疲劳驾驶/数据融合/高精度定位/疲劳驾驶检测Key words
traffic safety/face recognition/fatigue driving/data fusion/high precision positioning/fatigue driving detection分类
交通工程引用本文复制引用
孙健,唐旭,徐永能,苗梦格..融合机器视觉与高精度定位的高速公路疲劳驾驶行为检测方法[J].交通运输研究,2023,9(6):78-87,118,11.基金项目
国家重点研发计划政府间国际科技创新合作重点专项项目(2019YFE0123800) （2019YFE0123800）
国家自然科学基金项目(52072214) （52072214）
