论文标题:以人为中心的汽车主动安全预警信息系统研究
Study on Forewarning Information System of Human-centered Vehicle Active Safety
论文作者 廖传锦
论文导师 黄席樾,论文学位 博士,论文专业 控制理论与控制工程
论文单位 重庆大学,点击次数 106,论文页数 149页File Size9032k
2005-05-02论文网 http://www.lw23.com/lunwen_683215787/ 汽车;主动安全;预警;以人为中心
Vehicle; Active Safety; Forewarning; Human-centered
目前,汽车主动安全技术成为研究热点。大力研究开发如汽车防撞等主动式汽车安全技术,减少驾驶员的负担和判断错误,对于提高交通安全将起到重要作用。由于驾驶过程是一个高度智能化的过程,尽管学术界对汽车自动驾驶的研究投入了大量的精力,也取得了一定成果,但就目前计算机技术和人工智能的研究成果而言,尚不能建立一个确切的模型以全面真实地反映驾驶过程,因而,我们认为:安全预警,是目前提高行车安全的最为行之有效的技术手段。论文简要介绍了汽车主动安全技术、以人为中心的汽车主动安全思想的起源与发展,和汽车主动安全技术领域国内外的发展状况;针对当前研究状况提出:汽车主动安全技术当“以人为中心”,即:以驾驶员的安全为中心、以驾驶员的认知特性为中心和以驾驶员操作特性为中心。论文分析了行车过程中信息的特点与流程,以及驾驶过程的三个阶段,即:感知、决策和操作;探讨了汽车——驾驶员——环境这一闭环系统中,三者之间的相互作用关系,并以此得到了汽车——驾驶员——环境简易模型;提出了以人为中心的汽车主动安全预警信息系统的功能结构,结合安全预警的需要,提出了面向安全预警的以人为中心的汽车——驾驶员——环境模型。论文在分析了传统的以注意力“单源论”为基础的人的信息感知与处理模型基础上,采用注意力“多源论”思想,提出了注意力资源分为动态资源和静态资源的构想;建立了静态注意力资源的分配模型和动态注意力资源的功能结构模型;并在此基础之上,提出了新的“人(驾驶员)的信息感知与处理过程模型”;并针对该模型的各个功能环节提出了:基于模糊可测函数的信息特征指标匹配度模型、基于主成分分析法的信息特征指标优先权权重分配模型、基于隶属度的信息类别分类模型;构造了基于信息类别隶属度、类别优先权系数及人的倾向性系数的信息重要性指数;实现了基于信息重要性指数的信息选择。论文在分析了传统决策过程模型的特点与不足的基础上,针对驾驶过程的基本特点,提出了基于自学习机制的决策过程模型;针对该模型的各个环节,提出了:基于证据融合理论的特征匹配、基于特征相似度的一致性分析、基于模糊重心的行车状态安全性评估;构造了措施优选指数并以此实现驾驶操作措施方案选择;提出了基于降为映射变换的自学习机制模型。根据驾驶员的安全意识在驾驶过程中所起的作用,论文引入安全意识测度的概念,提出了安全意识测度的统计模型;利用汽车——驾驶员——环境系统安全等级模糊隶属度,通过统计模型对驾驶员的安全意识进行衡量,进行驾驶行为进行
The researching on technology of vehicle active safety is in the ascendant now. The R&D of technology of vehicle active safety, such as collision avoidance, to reduce the driver’s burden or misjudgment is important to improve the traffic safety. In despite of the large devotion of energy on automatic driving and the result of the researching, there is no one accurate model to describe the driving process veritably and completely, because the driving process is a high intelligentized process. So, it’s clear that forewarning is the effective technical means to improve the driving safety. The genesis and development of vehicle active safety technology, the idea of human-centered vehicle active safety and the states of arts of vehicle active safety technology are introduced briefly in this dissertation. In allusion to the researching status, one idea is presented that all the vehicle active safety technology should be human-centered, i.e. driver’s safety-centered, driver’s perceive identity-centered, driver’s operation identity-centered. The characteristic and flow of information and the three phases of driving, i.e. perception, decision-making, operation, are analyzed in this dissertation. A simple model of vehicle-driver-environment is presented based on analyzing the reciprocity of vehicle, driver and environment in the closed loop. The functional structure of human-centered vehicle active safety forewarning information system is formed and the model of forewarning oriented human-centered vehicle-driver-environment is presented to meet the need of safety forewarning. After analyzing the traditional model of human information perception and processing based on attention single resource theory, one conceive of dynamic resource and static resource of attention is formed based on the attention multi-resource theory. The distributing models of dynamic resource and static resource of attention are presented, and based on these models, a new model of human or driver information perception and processing is presented. In allusion to the new model’s functional phases, some models are formed, i.e., the model of matching degree of character indexes based on fuzzy measurable function, the priority weight distributing model of information character indexes based on HAP, the information sort classifying model based on subjection degree, the information importance exponent based on information sort subjection degree, sort priority weight and human orientation coefficient. The model of information selection based on the importance exponent is presented. Based on analyzing the characteristics and shortages of traditional decision-making models, a new decision-making model with self-learning mechanism is presented aiming at the basic characteristics of driving task. Some models or scheme are presented for this new model of decision-making, i.e. the feature matching model based on evidence fusion, correspondence analysis model based on weighted Euclidean distance, driving state safety evaluation model based on fuzzy center of gravity, refusal coefficient to realize the selection of driving operation scheme, the self-learning model based on mapping transposition. Driver’s safety consciousness measure is introduced to this dissertation according to the role of safety consciousness in the driving task. A statistic model of safety consciousness measure is presented. Driver’s safety consciousness will be weighted to audit the driving habits real-time by the model. A model of fatigue accumulation based on fatigue element is presented and the feature fatigue in the phases of perception, decision-making and operation is analyzed with the response curve of unit ramp function. An algorithm of fatigue analyzing based on safety consciousness measure is presented. The human-centered forewarning information system for vehicle active safety is introduced from three aspects, i.e. information capture, risk evaluation, structure of system hardware. Information capture is introduced from road information, host vehicle, obstacle information.
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