mmDrive:使用毫米波雷达进行细粒度疲劳驾驶检测

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Juncen Zhu, Jiannong Cao, Yanni Yang, Wei Ren, Huizi Han
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引用次数: 0

摘要

早期发现疲劳驾驶对驾驶员和行人的安全至关重要。传统方法主要采用摄像头和可穿戴传感器来检测疲劳特征,这对驾驶员来说是一种干扰。射频(RF)传感技术的最新进展使驾驶员身体反射的信号能够进行非侵入式疲劳特征检测。然而,现有的基于rf的解决方案只能检测部分或粗粒度的疲劳特征,从而降低了检测精度。为了解决上述限制,我们提出了一种基于毫米波的疲劳驱动检测系统,称为mmDrive,它可以检测来自不同身体部位的多个细粒度疲劳特征。然而,在驾驶过程中实现各种疲劳特征的准确检测遇到了实际挑战。具体来说,正常的驾驶活动和驾驶员不自觉的面部运动不可避免地会对疲劳特征产生干扰。因此,我们利用疲劳特征的独特几何和行为特征,设计有效的信号处理方法来去除疲劳无关活动的噪声。基于检测到的疲劳特征,进一步开发了疲劳判定算法,确定驾驶员的疲劳状态。模拟和真实驾驶环境的大量实验结果表明,检测点头和打哈欠特征的平均准确率约为\(96\% \),估计眨眼、呼吸和心跳速率的平均误差分别约为2.21bpm、0.54bpm和2.52bpm。提出的疲劳检测算法的精度达到\(97.63\% \)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
mmDrive: Fine-Grained Fatigue Driving Detection Using mmWave Radar
Early detection of fatigue driving is pivotal for safety of drivers and pedestrians. Traditional approaches mainly employ cameras and wearable sensors to detect fatigue features, which are intrusive to drivers. Recent advances in radio frequency (RF) sensing enable non-intrusive fatigue feature detection from the signal reflected by driver’s body. However, existing RF-based solutions only detect partial or coarse-grained fatigue features, which reduces the detection accuracy. To tackle above limitations, we propose a mmWave-based fatigue driving detection system, called mmDrive, which can detect multiple fine-grained fatigue features from different body parts. However, achieving accurate detection of various fatigue features during driving encounters practical challenges. Specifically, normal driving activities and driver’s involuntary facial movements inevitably cause interference to fatigue features. Thus, we exploit unique geometric and behavioral characteristics of fatigue features and design effective signal processing methods to remove noises from fatigue-irrelevant activities. Based on the detected fatigue features, we further develop a fatigue determination algorithm to decide driver’s fatigue state. Extensive experiment results from both simulated and real driving environments show that the average accuracy for detecting nodding and yawning features is about \(96\% \) , and the average errors for estimating eye blink, respiration, and heartbeat rates are around 2.21bpm, 0.54bpm, and 2.52bpm, respectively. And the accuracy of the fatigue detection algorithm we proposed reached \(97.63\% \) .
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CiteScore
5.20
自引率
3.70%
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