基于OBDII&EOBD的机动车监测系统分析与设计

针对国内个人用户、行业用户对实时性车辆状态、驾驶习惯和故障检修的需求,设计了一套机动车实时监测系统。基于OBDII&EOBD协议,车载OBD终端设备获取车辆故障码、地理位置、行车速度等数据信息,通过GPRS与基于J2EE架构的网站服务端建立通信。服务端对数据进行分析,从而对车辆的故障状态进行诊断,统计归纳驾驶员的驾驶习惯和行车状况,最终将车辆检测和统计分析的结果展现在网页客户端,或者i OS/Android移动手持设备上。视车辆的故障情况,驾驶员可以选择通过本系统与汽车维修厂建立联系,为驾驶员的行车安全、出行便利,以及交通管理和保险业提供参考建议和数据支持。     

汽车纵向主动避撞系统的研究现状与展望

针对现代交通安全问题,提出了一种辅助驾驶员行车安全的方法,即纵向主动避撞,主要围绕汽车纵向主动避撞系统中的行车信息感知与处理、安全距离模型、车辆动力学系统建模、车辆动力学控制四个关键技术进行叙述.各种先进的控制技术为研究汽车纵向主动避撞系统提供了新的机遇,它所衍生出的新功能新系统对汽车的安全性、舒适性及经济性等方面都有很大的提升.对汽车纵向主动避撞技术进行了综述,探讨了在研究汽车纵向主动避撞系统时所面临的问题,最后梳理出汽车主动避撞技术的发展趋势.     

清洁车智能监测与控制系统研究

针对目前市面上的清洁车容易因为驾驶员的疏忽造成清扫盘与路沿的碰撞,加速清扫盘的损坏,洒水作业容易波及路人,贴地工作的吸盘容易与减速带碰撞造成损失,不能自动根据垃圾量调节清扫功率等问题,提出了一种清洁车智能监测和控制系统。该系统采用超声测距模块避免碰撞,运用YOLOv4算法检测行人和减速带,使用VGG16网络进行垃圾量化进而调节清扫功率,通过CAN通信模块实现对清洁车设备的实时控制。该系统采用多线程实现多任务算法之间的协调与通信。行车实验表明,该系统的整体运行速度为每秒6-7帧,满足实时性。路沿距离检测及避障算法准确率为96%,垃圾量化算法准确率为90%,行人和减速带算法准确率为96.25%,准确率较高,能够有效提高清扫效率,降低设备损耗,提高行车安全。     

基于云计算的多特征疲劳驾驶检测系统研究与设计

随着私家车的普及,人们对汽车安全性、舒适性要求不断提高,通过对当前车载系统分析和汽车驾驶员疲劳驾驶状态研究,提出了一种基于信息融合的多特征疲劳驾驶检测方案。方案采用高性能嵌入式系统平台与云计算相结合的方式,首先,通过嵌入式系统采集驾驶员面部图像;然后,将数据传输到Face+ 云计算平台,分析当前驾驶人员身份、年龄与微笑程度;最后,采用数字图像处理技术计算驾驶员头部位移以及统计眼睛眨动规律,综合三种指标预测驾驶员是否处于疲劳状态,实时监测驾驶员驾驶全过程。当检测到驾驶员处于疲劳驾驶状态,则通过语音的方式提醒驾驶员注意行车安全、谨慎驾驶。测试结果表明：该方案检测精度高、实时性强,并且易于和车载系统整合并推广使用。     

智能安全驾驶监测系统设计与实现

针对当下发生的交通事故中及大部分是由于驾驶员酒后驾驶、违规驾驶或是突发身体疾病所导致的,设计出一种智能安全驾驶检测系统；系统以树莓派为和核心搭配智能手表、APP、酒精检测模块；系统可以实现监测驾驶员的酒精浓度,驾驶员的违规驾驶行为以及驾驶中的健康状况等功能,并通过移动端APP进行可视化展示；经测验,整个系统能够在驾驶途中稳定的运行,能够满足实时监测驾驶员的驾驶行为以及健康信息的需求。     

基于Kinect的疲劳驾驶综合监测系统研究

基于Kinect传感器研究设计一种驾驶员疲劳状态综合监测系统,通过对Kinect红外图像数据的预处理,减弱了夜晚光照不足的影响;进而利用Kinect提供的人脸识别功能获取驾驶员头部、嘴部、眼部等部位的特征信息,并利用RBF神经网络进行信息融合,分级判断驾驶员的疲劳状态;同时利用滑动平均法及数据库技术,使疲劳状态监测更加准确可靠。模拟实验结果表明,本系统在白天甚至夜晚都能较有效地监测驾驶员疲劳状态。     

基于局部离群因子的城市轨道交通行车安全防护设备状态监控系统

为了实现对城市轨道交通行车安全防护设备的智能化监控,提出基于局部离群因子的城市轨道交通行车安全防护设备状态监控系统设计方法。将安全设备的状态监控系统分为火灾自动报警系统、屏蔽门/安全门、广播系统、乘客信息系统等,结合对城市轨道交通行车安全防护设备安全影响因素,进行车辆段及设备应用配置分析,通过设施配备标准体系优化设计,实现对消防设施和综合监控设施配置,采用软件系统和预警接收单元联合控制的方法,采用局部离群因子检测,实现对城市轨道交通行车安全的烈度速报及视觉控制,实现城市轨道交通行车安全防护设备状态特征分析,提高状态监测的可靠性。测试结果表明,采用该方法进行城市轨道交通行车安全防护设备状态监控的可靠性较高,提高城市轨道交通的安全保障度。     

基于局部离群因子的城市轨道交通行车安全防护设备状态监控系统

为了实现对城市轨道交通行车安全防护设备的智能化监控,提出基于局部离群因子的城市轨道交通行车安全防护设备状态监控系统设计方法。将安全设备的状态监控系统分为火灾自动报警系统、屏蔽门/安全门、广播系统、乘客信息系统等,结合对城市轨道交通行车安全防护设备安全影响因素,进行车辆段及设备应用配置分析,通过设施配备标准体系优化设计,实现对消防设施和综合监控设施配置,采用软件系统和预警接收单元联合控制的方法,采用局部离群因子检测,实现对城市轨道交通行车安全的烈度速报及视觉控制,实现城市轨道交通行车安全防护设备状态特征分析,提高状态监测的可靠性。测试结果表明,采用该方法进行城市轨道交通行车安全防护设备状态监控的可靠性较高,提高城市轨道交通的安全保障度。     

基于便携式脑电设备的快乐和悲伤情绪分类

驾驶员情绪状态的识别对车辆主动安全技术的研究具有重要的应用价值.本研究通过情绪视频诱发的方法采集17位被试前额双通道脑电信号,提取不同情绪的脑电特征,并对数据进行降维处理后采用多种分类器进行情绪分类.结果显示,与单核分类器和集成学习分类器相比,基于梯度提升决策树（GBDT）算法得到快乐和悲伤的识别准确率最高.本研究为驾驶员情绪状态的实时监测和识别提供新方法,为提高行车的安全性提供了理论保障.     

自适应多汽车诊断协议的行车数据监测软件设计与实现

随着汽车的普及化、复杂化和对行车安全的重视,汽车制造、维修甚至普通驾乘人员对行车数据监测都提出了强烈的需求。在分析行车数据监测系统工作原理的基础上,提出一种能够自适应解析多种汽车诊断协议的行车数据监测软件设计方法,详细阐述软件的工作流程,功能模块构成,并给出关键代码。工程测试表明:所提出的自适应多汽车诊断协议的行车数据监测软件工作可靠,达到预期的设计目的。     

Monitoring drivers’ sleepy status at night based on machine vision

Driver fatigue is a chief cause of traffic accidents. For this reason, it is essential to develop a monitoring system for drivers’ level of fatigue. In recent years, driver fatigue monitoring technology based on machine vision has become a research hotspot, but most research focuses on driver fatigue detection during the day. This paper presents a night monitoring system for real-time fatigue driving detection, which makes up for the deficiencies of fatigue driving detection technology at night. First, we use infrared imaging to capture a driver’s image at night, and then we design an algorithm to detect the driver’s face. Second, we propose a new eye-detection algorithm that combines a Gabor filter with template matching to locate the position of the corners of the eye, and add an eye-validation process to increase the accuracy of the detection rate. Third, we use a spline function to fit the eyelid curve. After extracting eye fatigue features, we use eye blinking parameters to evaluate fatigue. Our system has been tested on the IMM Face Database, which contains more than 200 faces, as well as in a real-time test. The experimental results show that the system has good accuracy and robustness.     

疲劳驾驶多源性智能监测预警方法研究

对疲劳驾驶监测预警方法进行研究,可以避免驾驶员因疲劳驾驶产生的交通事故,减少因疲劳驾驶造成的人员伤亡和经济损失。当前的疲劳驾驶监测预警方法存在监测灵敏度低、可靠性差等问题,不能及时对疲劳驾驶的驾驶员进行报警,来避免交通事故的发生。为此,提出了疲劳驾驶多源性智能监测预警方法,首先将摄像头采集的驾驶员图像进行预处理,通过计算驾驶员图像信息的灰度值,得到驾驶员图像中像素的分布密度,为后续的监测和预警工作提供信息。其次,采用卡尔曼滤波算法对驾驶员的图像信息进行跟踪,得到驾驶员各个时间内的状态估计值,最后,通过计算驾驶员状态估计值判断驾驶员是否存在疲劳状态。实验结果表明,该方法的丢包率低、多源性高、抗干扰能力强、计算效率高。     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

A comparison of the cell phone driver and the drunk driver

OBJECTIVE: The objective of this research was to determine the relative impairment associated with conversing on a cellular telephone while driving. BACKGROUND: Epidemiological evidence suggests that the relative risk of being in a traffic accident while using a cell phone is similar to the hazard associated with driving with a blood alcohol level at the legal limit. The purpose of this research was to provide a direct comparison of the driving performance of a cell phone driver and a drunk driver in a controlled laboratory setting. METHOD: We used a high-fidelity driving simulator to compare the performance of cell phone drivers with drivers who were intoxicated from ethanol (i.e., blood alcohol concentration at 0.08% weight/volume). RESULTS: When drivers were conversing on either a handheld or hands-free cell phone, their braking reactions were delayed and they were involved in more traffic accidents than when they were not conversing on a cell phone. By contrast, when drivers were intoxicated from ethanol they exhibited a more aggressive driving style, following closer to the vehicle immediately in front of them and applying more force while braking. CONCLUSION: When driving conditions and time on task were controlled for, the impairments associated with using a cell phone while driving can be as profound as those associated with driving while drunk. APPLICATION: This research may help to provide guidance for regulation addressing driver distraction caused by cell phone conversations.     

Modeling driver behavior in a cognitive architecture

OBJECTIVE: This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system. BACKGROUND: Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. METHOD: An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment. RESULTS: This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. CONCLUSION: The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks. APPLICATION: The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.     

基于MEMS和GPS的驾驶行为和车辆状态监测系统设计

为了适应智能车辆辅助驾驶系统对驾驶和车辆状态监测的要求,利用MEMS惯性传感器自主设计了微惯性测量单元,并结合GPS设计了一种驾驶行为和车辆状态监测系统,实现对驾驶员操纵动作的感知、汽车6自由度运动状态参数和汽车运行车速的实时监测。介绍了MEMS传感器的选型,设计,安装和布置。实车道路实验结果表明:系统对驾驶员踩踏刹车踏板、离合器踏板和变换档位的操纵动作的感知效果较好,侧向加速度和方向盘转角的理论识别曲线与实车实验曲线在趋势上比较吻合。该系统为开发驾驶人员操纵动作自动识别系统提供理论基础和技术支持,也可为提高汽车行驶性和安全性提供重要的理论依据和工程应用指导。     

一种红外图像条件下的驾驶人眼睛定位算法

利用机器视觉对驾驶人面部器官状态进行监测和分析处理是安全辅助驾驶领域内的研究热点之一。首先介绍了驾驶人驾驶行为监测及预警系统的原理和结构,然后讨论了系统中驾驶人眼睛定位的技术要点。通过人类眼睛虹膜对不同波长的红外光吸收能力的不同,利用特制的红外光源获取到驾驶人面部的两种红外反射图像,利用图像处理算法进行处理从而获得驾驶人的眼睛位置,从而为下一步的眼睛状态及信息提取打下了良好的基础。实践证明,算法的实时性高,效果非常理想。     

Driver usage and understanding of adaptive cruise control

Automation, in terms of systems such as adaptive/active cruise control (ACC) or collision warning systems, is increasingly becoming a part of everyday driving. These systems are not perfect though, and the driver has to be prepared to reclaim control in situations very similar to those the system easily handles by itself. This paper uses a questionnaire answered by 130 ACC users to discuss future research needs in the area of driver assistance systems. Results show that the longer drivers use their systems, the more aware of its limitations they become. Moreover, the drivers report that ACC forces them to take control intermittently. According to theory, this might actually be better than a more perfect system, as it provides preparation for unexpected situations requiring the driver to reclaim control.     

An investigation of train driver visual strategies

Train driving is primarily a visual task; train drivers are required to monitor the dynamic scene visually both outside and inside the train cab. Poor performance on this visual task may lead to errors, such as signals passed at danger. It is therefore important to understand the visual strategies that train drivers employ when monitoring and searching the visual scene for key items, such as signals. Prior to this investigation, a pilot study had already been carried out using an eye tracking technique to investigate train drivers’ visual behaviour and to collect data on driver monitoring of the visual environment, Groeger et al. (2003) Pilot study of train drivers’ eye movements, University of Surrey. However, a larger set of data was needed in order to understand more fully train driver visual behaviour and strategies. In light of this need, the Transport Research Laboratory produced a methodology for the assessment of UK train driver visual strategies, on behalf of the Rail Safety and Standards Board and applied this methodology to conduct a large-scale trial. The study collected a wealth of data on train drivers’ visual behaviour with the aim of providing a greater understanding of the strategies adopted. The corneal dark-eye tracking system chosen for these trials tracks human visual search and scanning patterns, and was fitted to 86 drivers whilst driving in-service trains. Data collected include the duration and frequency of glances made towards different elements of the visual scene. In addition, the train drivers were interviewed after driving the routes, to try and understand the thought processes behind the behaviour observed. Statistical analysis of over 600 signal approaches was conducted. This analysis revealed that signal aspect, preceding signal aspect, signal type and signal complexity are important factors, which affect the visual behaviour of train drivers. Train driver interview data revealed that driver expectation also plays a significant role in train driving. The findings of this study have implications for the rail industry in terms of infrastructure design, design of the driving task and driver training. However, train driving is extremely complex and the data from this study only begin to describe and explain train driver visual strategies in the specific context of signal approaches. This study has provided a wealth of data and further analysis of it is needed to investigate the role of other factors and the complex relationships between factors during signal approaches and other driving situations systematically. Finally, there are important aspects of visual behaviour that cannot be examined using these data or this method. Investigation of other aspects of visual behaviour, such as peripheral vision, will require other methods such as simulation.     

Temporal separation and self-rating of alertness as indicators of driver fatigue in commercial motor vehicle operators

This on-road field investigation employed, for the first time, a completely automated trigger-based data collection system capable of evaluating driver performance in an extended-duration real-world commercial motor vehicle environment. The study examined the use of self-assessment of fatigue (Karolinska Sleepiness Scale) and temporal separation (minimum time to collision, minimum headway, and mean headway) as indicators of driver fatigue. Without exception, the correlation analyses for both the self-rating of alertness and temporal separation yielded models low in associative ability; neither metric was found to be a valid indicator of driver fatigue. In addition, based upon the data collected for this research, preliminary evidence suggests that driver fatigue onset within a real-world driving environment does not appear to follow the standard progression of events associated with the onset of fatigue within a simulated driving environment. Application of this research includes the development of an on-board driver performance/fatigue monitoring system that could potentially assist drivers in identifying the onset of fatigue.