一种车载疲劳驾驶检测方法

文章针对车辆驾驶员的疲劳检测,提供了一种使用图像处理的方法来对驾驶员的眼部信息进行提取,建立眼部状态判定模型,根据图像信息提取判断出闭眼的时长以及闭眼的频率分析判断疲劳行为,并对驾驶员进行提醒和辅助驾驶,以减少交通事故的发生。同时,该方法给出了驾驶员处于疲劳状态并预警,提出了疲劳等级划分,不仅检测驾驶员是否疲劳,还检测驾驶员的疲劳等级,并给出相应的提醒操作。     

基于多形态红外特征与深度学习的实时驾驶员疲劳检测

疲劳驾驶是导致车祸的重要诱因，严重危害道路交通安全，而车辆行驶过程中的光照条件变化、驾驶员姿态调整和眼镜遮挡等因素将对疲劳检测任务产生不利的影响。针对以上问题，提出了基于深度学习的驾驶员疲劳检测算法。首先，使用850nm红外光源补光，在复杂光照和遮挡形态下采集驾驶员的面部图像；其次，利用红外图像中的多种特征，通过级联CNN确定人脸边框和特征点位置，提取眼睛区域并识别眼睛的睁闭状态；最后，将眼睛状态识别结果和连续图像中的特征点坐标差值输出至LSTM网络，检测驾驶员疲劳状态。实验结果表明:该疲劳检测算法的准确率可达94.48%，平均检测时间为65.64ms。     

基于SVM的疲劳驾驶人脸图像特征检测算法

针对传统图像识别算法对疲劳驾驶检测精度差、准确率低的缺陷,提出了一种利用人脸图像特征提取的疲劳驾驶检测方法。首先将实时采集到的车辆驾驶员面部图像进行预处理,借助Dlib检测出图像中的人脸区域并进行人脸图像特征点的标注,然后使用基于眼睛纵横比(Eye Aspect Ratio,EAR)的方法进行图像中人眼疲劳特征的识别,基于嘴唇纵横比(Mouth Aspect Ratio,MAR)的方法进行图像中嘴部疲劳特征的识别,最后利用支持向量机(SVM)的方法将两种特征融合起来进行疲劳驾驶检测。实验表明,该方法可以准确地定位出特征点,疲劳检测的识别率达84.29%,可以有效地识别出疲劳状态。     

基于人眼状态信息的非接触式疲劳驾驶监测与预警系统

疲劳驾驶预警系统对保障驾驶员的安全驾驶具有十分重要的作用。以驾驶员人眼图像信息处理为基础,建立了离散单位时间内非正常状态时间所占百分比疲劳判断模型,实现了对驾驶员疲劳状态的监控与预警。通过近红外光源对人眼主动照明,采用互补金属氧化物半导体摄像头实现对人眼图像信息的采集,基于Adaboost算法实现人眼准确定位,利用Harris强角点检测人眼中心区域,得到眼睛的视线状态信息,根据疲劳判断模型,设计可调的预警阈值,实现驾驶员疲劳状态的分级预警。实验结果表明:在一定条件下,系统判断响应时间为1.5 s,虚警率为4%,具有抗干扰性强和实时性好等特点。     

基于ZYNQ AP SoC的安全驾驶系统设计

针对系统对实时图像处理的需求,本文提出了一种基于ZYNQ AP SoC的安全驾驶系统设计方案.本系统由ZYNQ架构中的PL(FPGA)部分负责驱动CMOS摄像头,将采集的图像进行灰度转换,传给PS(ARM)部分运行Adaboost算法,对图像进行人脸检测,从而获取驾驶员的眼睛和嘴巴的坐标值、面积值和张开度,并利用OpenCV的PERCLOS算法制定疲劳状态标准,给出预警信息.同时,ARM通过USB驱动摄像头,实现行车记录,并通过酒精浓度传感器采集车内酒精浓度,实现酒驾预警.通过实验表明,本系统性能稳定,实现了保障安全驾驶的目的.     

疲劳预警的长途客车监控系统的设计

针对近年来国内频发因长途客车的超员、超速以及驾驶员的疲劳驾驶等引起的安全的问题,提出一种疲劳预警的长途客车监控系统的设计。在Linux平台下实现乘客图像数据的采集、乘客行李物品信息的登记和关联、客车车辆的实时定位和监控以及对驾驶员进行实时的疲劳检测和预警等。通过车载摄像头实现视频图像数据的实时采集,利用GPS模块对客车进行实时的定位,采用RFID技术完成乘客行李物品信息的登记和关联,采用人脸识别术对乘客进行身份的认证,通过人眼运动的检测进行疲劳驾驶预警,通过多传感器融合技术进行超员检测,通过GPRS网络把客车的状态信息传送到车辆监控中心。     

基于疲劳监测的驾驶风险评估系统设计

疲劳驾驶已成为影响道路交通安全的重要因素，引入驾驶员实时疲劳检测结果，综合“人—车—路—环境”4个方面建立了基于Logistic回归模型的驾驶风险评估系统。将基于面部多特征的疲劳驾驶检测结果作为驾驶员驾驶状态输入，将通过高德地图应用程序接口（application program interface,API）获得的驾驶员行驶道路线形以及当前环境能见度和车辆类型哑变量处理与变量赋值后作为自变量输入，建立了驾驶风险评估系统。在背光与对光的情况下模拟车载环境，分别对系统进行测试，结果显示疲劳状态识别率达90%以上；在评估模型方面，通过SPSS检验，模型总体有意义且拟合优度较高。     

一种考虑驾驶员姿态及光照因素的疲劳驾驶稳健检测算法

考虑疲劳驾驶检测过程中容易出现的偏头情形和光照因素影响,提出一种疲劳驾驶稳健检测算法。在实现Adaboost和主动性状模型相结合的人眼定位基础上,算法首先通过归一化人脸图像旋转等手段,使检测系统可以适应驾驶过程中经常出现的驾驶员偏头情形;其次通过补充人脸训练样本和引入直方图均衡等手段,使其可以更好地适应驾驶中出现的各种光照环境。最后利用Perclos算法对驾驶员疲劳状态进行判定。对模拟视频及车内采集的真实驾驶样本视频进行检测实验,结果表明稳健检测算法可以更准确定位人眼的位置。不仅可以有效的适应偏头情况,并且可以消除光照因素的影响,提升了检测系统的稳健性。     

基于Matlab的人眼疲劳度检测

研究旨在协助驾驶员提高行车安全,减少疲劳驾驶带来的隐患.检测汽车驾驶员的唤醒状态,若得到疲劳信息,则发出警报.边缘检测算法,边界跟踪算法以及人眼定位算法以实现对驾驶员的监测;设计中定义眼睛闭合度的参数,衡量所采集到的眼睛图像的纵横之比,使系统对不同的人或同一个人的不同状态进行测量,保证实际应用价值.     

基于ADSP-BF548的驾驶员疲劳检测系统设计

为防止由疲劳驾驶引发的交通事故,采用基于视觉特征PERCLOS的疲劳检测算法,并以ADI公司ADSP-BF548处理器为算法硬件平台,实时检测驾驶员的疲劳状态。本系统通过车载摄像头实时拍摄驾驶员人脸图像,然后经帧差、投影、模板匹配等图像处理方法正确定位驾驶员的人眼位置,最后根据单位时间内人眼闭合时间所占比,超过阈值则适时预警。实验表明,基于ADSP-BF548的驾驶员疲劳检测系统具有良好的准确性和实时性。     

基于红外差分图像的驾驶员疲劳检测系统

通过对各种疲劳检测方法进行比较分析,提出了一套基于PERCLOS的驾驶员疲劳检测系统总体设计方案。该系统采集图像使用红外光源,利用人眼对两种波长(850nm/950nm)红外光线的反射率的明显差异,应用图像差分的方法,采用直方图均衡化、二值化、膨胀、腐蚀等图像分析手段,同时利用Kalman(卡尔曼)滤波器对眼部位置进行跟踪预测,实现准确定位、跟踪并识别出驾驶员眼睛的睁开和闭合过程,通过分析眼睛闭合时间来判断疲劳程度。     

Design and implementation of a real-time eye tracking system

Driver fatigue severely affects driver's alertness and ability to drive safely. There are vital problems related to drivers fatigue on driving of trains, vehicles and airplanes. Therefore, the driver fatigue research is important. In this paper, we first study the impact of eye locations on face recognition accuracy, with Haar-like feature and AdaBoost classifier, face and eye area can be detected quickly and accurately. In the part of eye tracking, cam-shift based mean-shift algorithm is used to track the eyes. This method could automatically adjust the size of tracking window according to the different posture of driver. The performance of our eye detection method is validated by using image database with more than 6000 pictures. In addition, our real-time eye tracking system has been tested on railway line segment (China). There are 5 train drivers involved in the experiment. The validation shows that our eye detector has an overall 93% eye detection rate.     

Seat Belt Vibration as a Stimulating Device for Awakening Drivers

This paper presents a safety driving system that uses a seat belt vibration as a stimulating device for awakening drivers. The vibration stimulus was composed of pulsation tension, which was applied by the seat belt motor retractor. Magnitude, duration, and repetition rate of the additional tension were the major parameters that determined the awakening effect of the stimulus. We constructed a driving simulator, which was able to induce driver's drowsiness. In the experiments using the driving simulator, the driver's drowsiness was detected by changes in the driver's eye movements measured by electrooculography (EOG) and/or changes in facial expression of the driver monitored by the examiners through a video camera, subjective evaluation, and lane deviation. Exerting additional tension of 130 N for 3 cycles at duration and interval of 100 ms was the most effective pattern for awakening the driver without causing discomfort.     

基于脑电识别与车辆操纵特征的驾驶疲劳检测分析

本文以脑电识别与车辆操纵特征为切入点,通过模拟疲劳驾驶实验,将脑电识别与车辆操纵特性相结合来检测驾驶员的疲劳状态.通过对脑电信号的S变换分析,发现不同驾驶时刻其变换时频谱图存在显著差异,可用来区分驾驶过程中驾驶员的精神状态,结合车辆操纵特征参数,得到操纵特征与疲劳状态的关系,为脑电识别与操纵特征的驾驶疲劳检测的有效性提供一定的理论和实验基础.     

Fatigue driving detection based on Haar feature and extreme learning machine

As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers’ fatigue driving by using the human eye tracking technology.     

基于光学多传感器场景信息的视觉辅助驾驶技术

为满足坦克、装甲车辆等军用车辆的闭舱、无窗驾驶需求,研制了一套新型辅助驾驶系统。系统将分布于车辆四周的多路光学传感器获取车辆近身场景,通过全景拼接算法得到车辆近身360°的全景鸟瞰视频,该视频显示于车载显示屏,用于车辆通过窄道、有障碍物等特殊路段,或倒车时,驾驶员观看。同时,在车辆通过常规路段时,上述视频可根据驾驶员头部扭转角度,裁选出符合人眼观察视角的车外场景视频,传输至驾驶员显示头盔上,供驾驶员观看。如遇特殊情况,车载显示屏会报警,驾驶员将回归车载显示屏的观看。其中,驾驶员头部位置确定方法采用了红外LED光源图像定位技术和MEMS惯性器件定位技术。在实验室,搭建模型小车,验证全景鸟瞰视频生成技术和头盔自由视点观察技术。此外,还使用真实车辆进行了跑车实验。实验结果表明,上述系统可满足闭舱无窗车辆在常规路况下行驶,速度可达40 km/h,同时可辅助车辆窄道行车、障碍物绕行和倒车等事项顺利进行。     

基于3G视频的驾驶员疲劳状态检测方法

疲劳驾驶已成为交通事故发生的主要原因之一。文中提出了一种基于3G视频的人眼疲劳检测方法。通过DirectShow技术对视频流抓取视频帧,采用肤色聚性特征进行人脸定位,基于灰度信息进行人眼定位与追踪,并采用Perclos方法进行疲劳判断。通过此方法,可以及时了解驾驶者的疲劳状态,有效预防疲劳驾驶。     

基于脑电图识别结合操纵特征的驾驶疲劳检测分析

交警部门在进行道路安全管理时,对疲劳驾驶的人员进行有效的驾驶疲劳检测,是辨别疲劳驾驶人员前提与基础。本文基于脑电图识别结合操纵特征为切入点,通过选取的样本进行驾驶疲劳实验,将脑电图识别与车辆操纵特性相结合来检测驾驶员的疲劳状态。     

Driver fatigue detection based on deeply-learned facial expression representation

Driver fatigue detection is a significant application in smart cars. In order to improve the accuracy and timeliness of driver fatigue detection, a fatigue detection algorithm based on deeply-learned facial expression analysis is proposed. Specifically, the face key point detection model is first trained by multi block local binary patterns (MB-LBP) and Adaboost classifier. Subsequently, the eyes and mouth state are detected by using the trained model to detect the 24 facial features. Afterwards, we calculate the number of two parameters that can describe the driver's fatigue state and the proportion of the closed eye time within the unit time (PERCLOS) and yawning frequency. Finally, the fuzzy inference system is utilized to deduce the driver's fatigue state (normal, slight fatigue, severe fatigue). Experimental results show that the proposed algorithm can detect driver fatigue degree quickly and accurately.     

Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles

This paper deals with the development of Human-Centric Intelligent Driver Assistance Systems. Rear-end collisions account for a large portion of traffic accidents. To help mitigate this problem, predictive braking systems and adaptive cruise control systems have been developed. However, these types of systems usually rely solely on the vehicle and vehicle surround sensors, either ignoring the human component of driving or learning the driver's control behavior using only these sensors. As with all human-computer interfaces, this has the potential to work against the driver, distract the driver further, or even annoy the driver so that the driver ignores or disables the system. It is, therefore, important to directly take the driver's intended actions into account when designing a driver assistance system. By using a probabilistic model for the system, warnings and preventative measures can be constructed based on varying levels of situational severity and driver attentiveness and intent. The research is based upon carefully conducted experimental trials involving a human subjects driving in natural manner and on typical freeways in the USA. The experiments, designed by inputs from cognitive scientist, were conducted in a specially designed instrumented vehicle to record important cues associated with driver's behavior, vehicle state, and vehicle surround in a synchronized manner. Quantitative results and analysis of the experimental trials are presented to show the feasibility and promise of this framework to predict the driver's intent to brake, the need for braking given the current situation, and at what level the driver should be warned