虚拟驾驶环境中车辆智能体的驾驶行为模型

建立虚拟交通环境的多智能体结构,分析车辆智能体的驾驶行为分层模型以及感知、决策和操作等过程。采用模糊专家系统建立车辆智能体的驾驶行为模型。为模拟现实中的驾驶员行为特性,加入驾驶员因子,使驾驶模拟器的虚拟交通环境更符合现实。运用OpenGVS产生和显示实时交互的虚拟驾驶场景。结果表明该模型能体现实际驾驶行为的多样性、随机性和模糊性。该模型通用有效,它使驾驶模拟器的虚拟交通场景更真实满意。     

基于模糊神经网络的自动驾驶决策系统研究

驾驶决策过程中,驾驶行为常受到人、车、路、环境等多源信息的刺激和影响。由于信息处理能力有限,驾驶员对多源信息无法同时实现知识获取与表示,以致有时不能准确、快速地进行驾驶决策,易引发交通事故。为了解决这一问题,针对驾驶员的控制行为进行了分析,并基于模糊控制和神经网络等理论知识,建立了智能车辆驾驶决策机制的模糊神经网络模型。该模型的建立有助于驾驶人员做出更安全、更有效的控制策略。仿真结果表明,用模糊神经网络推理系统有较高的推理速度,能实时、准确地识别当前的驾驶行为和预测下一时刻的驾驶决策,为智能车辆中自动驾驶系统的仿真和实现提供了理论指导和可行性依据。     

驾驶行为的初步研究

疲劳驾驶对交通安全的危害日益严重，通过给驾驶行为建模来研究疲劳行为，主要介绍了给驾驶行为建模的主要流程，信号采集，对方向信号进行傅立叶级数展开来提取特征值，确定特征值的个数等。通过对驾驶员正常的驾驶行为进行建模，当驾驶员处于疲劳状况下驾车偏离正常模型时则能够有效的区别开。     

虚拟驾驶员决策行为模型研究

驾驶决策行为是驾驶行为研究的重要内容.为提高驾驶决策行为建模与仿真的可信度,提出了基于分层的驾驶员决策行为模型.将驾驶决策分为策略层、方法层、行动层和车辆控制层四个层次.重点对驾驶员行动层的决策行为进行实现.使用两点法和PID方法相结合,计算车辆转向角度,使用反应点跟车模型计算车辆纵向行驶速度.通过驾驶员跟车行为仿真,分析了跟车行为模型的稳定性和逼真性,说明了驾驶员决策行为模型的可信性,使驾驶行为模型的输出更加真实.     

智能驾驶中的行为辅助压力感知方法

人的压力与其行为紧密相关,特别是在智能驾驶时,驾驶员压力感知对实现辅助驾驶具有巨大的应用潜力.现有压力感知方法多用于静态环境,检测过程也缺乏便捷性,难以适应高度动态的智能驾驶应用需求.为了实现智能驾驶中自然、准确和可靠的压力检测,提出一种基于可穿戴系统的行为辅助压力感知方法.该方法基于行为伴随实现压力检测,并基于多指标执行压力状态判别,能够有效提高压力检测准确度.其基本原理在于每个人在不同压力状态下的生理特征和行为模式不同,会对压力相关的PPG数据和行为相关的IMU数据产生独特影响.首先使用嵌入多传感器的可穿戴手套测量驾驶员的生理和运动信息,通过多信号融合技术获得可靠的生理行为指标,最终使用泛化性能较好的SVM模型分类驾驶员的压力状态.基于所提出的方法在模拟驾驶环境下部署了验证实验,实验结果显示,压力分类精确度可达到95%.     

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。     

基于人类视觉皮层双通道模型的驾驶员眼动行为识别

驾驶员的危险行为会增加交通事故的发生率,目前对驾驶员行为的研究中大多通过面部识别等方法对异常行为如疲劳驾驶、接电话等进行识别.这种方法仅客观地对驾驶员行为进行分类,而忽略了他们在驾驶过程中的主观心理.眼动仪是记录和分析驾驶员眼动数据的有效工具,可以清晰地了解驾驶员的想法并总结其视觉认知模式.因为目前还没有针对驾驶员眼动...     

基于多源信息融合模型的高龄驾驶员安全性评估方法研究

针对老年人驾驶中遇到的问题,本文设计了一个适合于高龄驾驶员的辅助驾驶系统。首先分析了高龄驾驶员驾驶行为随着驾驶速度和持续驾驶时间的关系。接着,结合路面信息和驾驶行为的特征,采用模糊信息融合方法给出了驾驶环境安全度的综合评价。通过模糊信息融合推理,可以使高龄驾驶员对自己的驾驶行为和周围状况有一个全面的认识。随后通过对驾驶行为和事故日志的挖掘,寻找不同驾驶员的特征,通过调整推理规则,提供适合于不同驾驶员的评价。本文所设计的辅助驾驶系统充分考虑了高龄驾驶人员的行为特征,实时为高龄驾驶者提出驾驶行为调整策略,全面提升高龄人员驾驶行为的安全性和舒适性。     

基于心理物理综合认知结构的微观交通仿真模型

针对驾驶行为的不确定性,在分析驾驶员心理-物理微观特性的基础上,构建了基于驾驶员任务集聚的心理-物理综合认知结构及其各行为运行模式下微观交通仿真的车辆跟驰模型.运用五轮仪试验系统所采获的实际数据和多元统计分析的数学方法对模型进行了标定,并使用与模型标定过程所用到的数据不同的另一部分数据验证了模型的有效性.结果表明,该文所提出的模型和算法能够很好地刻画驾驶员心理-物理行为特性的复杂性,再现人车单元的实际动态行为,为网络交通流一体化协同仿真和智能运输系统研究提供理论基础.     

营运车辆驾驶行为的研究与应用

我国公路运输里程数居世界前列,且大部分的客货运输工作都是由营运车辆来完成.有数据显示,大部分的重特大运输事故与营运车辆有关.因此需重视营运车辆的运行状况,特别是重视驾驶员的驾驶行为,以减少车辆事故的发生.论文通过对车联网数据进行分析、确定驾驶行为指标及各指标得分细则并使用K-means聚类方法对驾驶行为进行分级,最终建立评估模型,客观评估驾驶行为的优劣.实际结果表明,该评价模型为驾驶员行为习惯的综合分析提供了一种可行的方法.     

基于车辆行驶数据的驾驶人行为谱分析方法

针对我国驾驶人行为谱的研究尚不完善，专业领域内没有相应的行为谱分析工具的问题，提出了一套针对营运客车的完整的驾驶人驾驶行为谱体系并设计了一套分析工具。首先，设计并定义了驾驶人行为谱的特征指标和评价指标；其次，给出了驾驶人行为谱的特征指标分析、计算方法，采用基于马尔可夫链蒙特卡洛采样和离群点剔除的K-means算法对驾驶人的驾驶风格进行分析，采用回归学习对驾驶人的驾驶技能进行分析；然后，设计了基于车联网、大数据的驾驶人行为谱的基础数据采集和预处理方法；最后，采用Java语言、Spring MVC架构开发出驾驶人行为谱分析工具。将机器学习中的数据挖掘、数据分析算法与交通安全领域相结合，对完善我国驾驶人行为谱框架体系具有理论意义，为我国驾驶人行为谱的研究提供了一个科学、定量化分析的工具，对交管部门规范驾驶人驾驶行为、提高道路安全指数、制定合理的交通安全管理策略具有指导意义。     

基于车辆周期行为的UBI设计

随着定位与导航技术的快速发展,使基于位置信息的各种应用成为可能.在车辆保险领域,让那些驾驶行为习惯良好,以及有着丰富驾驶经验的驾驶员可以最大程度享受到保费上的优惠,有助于减少不安全驾驶行为.通过对车辆周期性行为进行分析,可以评估车辆行驶风险.讨论了周期性行为数据的采集方法,以及用离散傅里叶变换等方法对数据进行分析、归纳.保险公司根据差异化的驾驶行为和特征给出差异化的保费定价,使保费的制定更加合理.     

用户驾驶行为建模的研究和应用

随着汽车工业的快速发展,车载环境成为人们日常生活中最重要的私人空间之一。同时,随着车载设备的增多,车载服务也日益丰富,然而大多数车载服务都是针对大众而设计的,缺乏个性化支持。通过车载环境下的用户建模,可以使车载环境成为以用户为中心的个性化空间,从而为用户提供个性化车载服务。车载服务的一个关键目标是提高用户的行车安全,它与用户的驾驶行为息息相关。因此,对用户驾驶行为的建模是车载环境下用户建模的一个重要组成部分。对车载环境下的用户建模进行了研究,提出并实现了用户驾驶行为的建模方案,并在其基础上构建了个性化车载服务。     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

基于Speed Dreams的驾驶员应激响应分析系统

为了研究应激状态下的驾驶员行为,提出了一种驾驶员应激响应分析系统.硬件系统以罗技赛车方向盘为基础并进行相应的机械改造,软件系统采用Speed Dreams引擎创建典型应激场景;采用Joystick监控方法以及多线程通讯技术,高速动态采集并记录驾驶行为.对采集数据进行滤波、去噪、微分等处理和分析后,得出驾驶员的应激响应时间及数据间的相关规律.该研究成果可以为应激状态下的驾驶员行为研究提供可靠的数据支持.     

In-depth behavior understanding and use: The behavior informatics approach

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of BI in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.     

Identification of aggressive driving behavior of online car-hailing drivers based on association classification

With the rapid development of online car-hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10-fold cross-test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule-based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty-six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U-turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.     

变量化分析在传动滚筒的结构设计中的应用

传动滚筒是胶带机的关键部件,传动滚筒的结构分析是其结构设计的重要环节。本文在机械设计软件系统I-DEAS的环境中,建立了传动滚筒的有限元实体模型。运用变量化分析的基本原理对工作载荷下的传动滚筒进行线性静力分析,根据分析结果得出滚筒设计要点。     

Development and application of an integrated traffic simulation and multi-driving simulators

Professional virtual reality experiment tools, including driving simulators and traffic simulators, have their strengths and weaknesses. The integration of the two simulators will enhance the ability of both traffic modeling and driving simulation and present a new area of applications. This paper develops, implements, and validates an experimental platform that integrated a traffic simulator with multiple driving simulators (TSMDS). As a connected multi-user framework that allows multiple drivers who are simultaneously handling many driving simulators, it not only allows driver behavior experiments to be more accurate, controlled, and versatile but also simulates special driving behavior or multi-vehicle interactions under more realistic traffic flow environments. To validate the performance of TSMDS, 27 drivers were recruited to attend the lane changing experiments at a recurring on-ramp bottleneck and left-turn experiments at a two-phase signalized intersection in Shanghai. Both experiments required several drivers to drive the TSMDS and fulfill several complicated lane changing/crossing behaviors through their interaction. The results show that both the participants’ response and lane changing/crossing data that were obtained from the experiment are consistent with the field observation, which confirms the validity of the integrated platform.