智能运输系统的信息物理-事理（WS）研究
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摘要
智能运输系统(ITS)作为道路运输系统在信息时代的发展形态，不仅融入了以信息技术为主体的现代科技最新成果，呈现出历史上多次变革所不具有的深刻性和复杂性，而且由于与传统运输方式截然不同的运输服务特征，即：载运工具汽车的乘用者一般同时也是驾驶员，智能车辆实现及其运用一直是ITS的核心问题，并且因为智能车辆驾驶主体——人的因素的约束，使这一问题集中表现在以出行需求与驾驶员能力为中心的信息处理上。因此，本文系统研究了贯穿ITS各层次的信息物理-事理(WS)，提出了ITS的理论核心，即信息熵的作用和耗散结构的运动规律；并系统辨识了智能车辆的人因信息问题，给出了系统解决方案和关于信息测度及融合的关键算法。
论文针对ITS信息现象，采用“从总体到局部，宏观规律与微观模型相结合”的研究思想，将非平衡态热力学与耗散结构理论引入ITS领域，从宏观运输系统的进化史到微观控制技术的发展历程，研究并论证了运输系统的熵变规律和耗散结构特性；揭示了ITS信息技术及其信息运动的负熵实质，提出了ITS的耗散结构机理。这是论文对ITS信息现象总体把握的核心。论文进一步就该系统规律的微观实现，针对智能车辆驾驶决策支持信息环境，这一集合了人、车、路、信息各要素的交通工程对象，进行了系统研究。论文定义、分析了驾驶决策支持信息环境的基本概念(DDSEI)、信息结构及其内部关系；提出了关于该信息环境特性及规律的一般系统理论描述；研究了其中驾驶员处理信息的四阶段模式；给出了DDSEI设计的概念模型。由DDSEI的研究，论文综合传统人因概念和智能车辆驾驶过程的信息特性，提出并讨论了人因复合信息的理念及其主要特性；联系人因特性的复杂性，论文总结了人因评价方法的主要成果，分析了现有算法的局限性，同时结合对国外主要智能车辆开发试验的结果分析，建立了ITS条件下道路行车系统可靠性评价体系；论文根据驾驶员获取信息和DDSEI信息状态的转移机理，提出以转移信息熵优化DDSEI设计，给出了转移信息熵表达式，定义了DDSEI的自适应机制，建立了由用户模型、任务模型和交互模型构成的自适应人-机界面体系结构；并分别就这三种模型的内容及实现作了原理和功能研究，给出了自适应人机界面的工作流程。
西南交通大学博士研究生学位论文 第if页
论文的另一个核心是在ITS信息现象的微观运动研究中，针对人因复合
信息特性剖析了现有语义测度计算的局限性，在提出语义树系列定义的基础
上，建立了由信息深度、信息粒度和形式测度构成的语义立体测度的概念和
算法，并给出了算法证明；同时论文对照驾驶过程的信息运动特性，建立了
DDSEI语法测度的一般性表达式，导出了某一信息源的期望效用、DDSEI
的期望效用、以及DDSEI效用指数的计算公式；由此得出了驾驶决策支持信
息人因复合测度的高维结构和量化公式。作为ITS信息现象的微观运动研究
的另一个重要方面，论文通过对信息融合理论和方法、特别是针对其在交通
运输领域的应用，进行了全面总结，并以此面向ITS发展需要提出了人因信
息融合的拓展理念；论文在构建DDSEI信息服务层次模型的基础上，建立了
DDSEI的多智能体（MAS）设计模型，深人讨论了其内部机理和模型结构，
并对这一解决方案的实现关键作了详解，包括实时数据库方案的数据模型。
主要融合技术应用的概念模型和异质信息融合算法等。由此完成了对DDSEI
信息融合设计从系统模型到关键技术的全面解决方案。
Intelligent Transportation System (ITS), a developing conformation of transportation system in the information times, has been integrating a variety of advanced technologies, especially information technology. It has the profoundness and complexity that the evolutions of transportation system did not have ever before. Because ITS has particular service characters totally different from traditional conformations, that is, travelers by automobiles are often the drivers of the vehicles, intelligent vehicle (IV) and its applications are the core of ITS applications. And because of the human factor constraints due to IV drivers, the key to IV applications is the information management or operation based on the needs in travel and driver capacity, including information measurement, integration and fusion. Thus, it is of theoretical and practical significance to study systematically on the Wuli-Shili* of information phenomenon stemming from every layer of ITS.
Aimed at information phenomenon of ITS, the contents are arranged as "from overall to partition, and macrolaw illustration in combination with microalgorithm creation". This paper introduce nonequilibrium thermodynamics and the theory of dissipative structures into transportation fields, especially into ITS. It studies, expounds and proves the entropy variation laws and dissipative characters of transportation system, reveals the negative entropy, the essance of information phenomenon of ITS, and presents the dissipative mechanism of ITS. This paper studies further driving decision support environment of information (DDSEI) of intelligent vehicle, which is composed of all the elements, i.e. drivers, vehicles, roads and information. The concept of DDSEI is defined in this paper, the information structure and internal relations of it are analized, then the general system description about DDSEI is set up. After the four-stage model for information managing of drivers is studied, the
* Wuli means objective systems and the scientific knowledge resulted from the human beings' research and discover on the world; Shili means how to generate some methods based on real existence, in order to direct the human beings' practices for more efficiency, benefits and perfection. Wuli - Shili (WS) is cited from the concept of Wuli - Shili - Renli (WSR) that was put forward firstly by Prof. Gu Jifa, and it has received broad recognition and is being applied in many fields.
concept model for DDSEI designing is proposed.
This paper summarizes traditional concepts of human factors and the Information characters of intelligent vehicles, proposes the concept of human factor compound information and discusses its mostly characters. According to the complexity of human factors, this paper analizes the restricts of current algorithms, design an evaluation index architecture for driving on highways. Based on the diversion mechanism of information states in DDSEI and information acquisition process of drivers, this paper recommends to optimize the DDSEI designs with diversion information entropy and presents the formula of the entropy. And the adaptive mechanism of DDSEI is defined and its adaptive man-machine interface architecture is also established that is made up of user model, task model and interface model, then the study of the principles and functions about all the three models is carried out and the working flow of the adaptive interface is achieved.
According to the characters of the human factor compound information and the limits of the current working methods, this paper puts forward a series of defines focused on the concept of semantic tree, creats the concept of multidimentional semantic measurement embodying information depth measurement, information granulation measurement and information form measurement, and the relative algorithms or formulas are given out. At the same time the general function description of syntactic information measurement of DDSEI is also created, and the expectation pragmatic information measurement of a information source, and of DDSEI, pra
论文针对ITS信息现象，采用“从总体到局部，宏观规律与微观模型相结合”的研究思想，将非平衡态热力学与耗散结构理论引入ITS领域，从宏观运输系统的进化史到微观控制技术的发展历程，研究并论证了运输系统的熵变规律和耗散结构特性；揭示了ITS信息技术及其信息运动的负熵实质，提出了ITS的耗散结构机理。这是论文对ITS信息现象总体把握的核心。论文进一步就该系统规律的微观实现，针对智能车辆驾驶决策支持信息环境，这一集合了人、车、路、信息各要素的交通工程对象，进行了系统研究。论文定义、分析了驾驶决策支持信息环境的基本概念(DDSEI)、信息结构及其内部关系；提出了关于该信息环境特性及规律的一般系统理论描述；研究了其中驾驶员处理信息的四阶段模式；给出了DDSEI设计的概念模型。由DDSEI的研究，论文综合传统人因概念和智能车辆驾驶过程的信息特性，提出并讨论了人因复合信息的理念及其主要特性；联系人因特性的复杂性，论文总结了人因评价方法的主要成果，分析了现有算法的局限性，同时结合对国外主要智能车辆开发试验的结果分析，建立了ITS条件下道路行车系统可靠性评价体系；论文根据驾驶员获取信息和DDSEI信息状态的转移机理，提出以转移信息熵优化DDSEI设计，给出了转移信息熵表达式，定义了DDSEI的自适应机制，建立了由用户模型、任务模型和交互模型构成的自适应人-机界面体系结构；并分别就这三种模型的内容及实现作了原理和功能研究，给出了自适应人机界面的工作流程。
西南交通大学博士研究生学位论文 第if页
论文的另一个核心是在ITS信息现象的微观运动研究中，针对人因复合
信息特性剖析了现有语义测度计算的局限性，在提出语义树系列定义的基础
上，建立了由信息深度、信息粒度和形式测度构成的语义立体测度的概念和
算法，并给出了算法证明；同时论文对照驾驶过程的信息运动特性，建立了
DDSEI语法测度的一般性表达式，导出了某一信息源的期望效用、DDSEI
的期望效用、以及DDSEI效用指数的计算公式；由此得出了驾驶决策支持信
息人因复合测度的高维结构和量化公式。作为ITS信息现象的微观运动研究
的另一个重要方面，论文通过对信息融合理论和方法、特别是针对其在交通
运输领域的应用，进行了全面总结，并以此面向ITS发展需要提出了人因信
息融合的拓展理念；论文在构建DDSEI信息服务层次模型的基础上，建立了
DDSEI的多智能体（MAS）设计模型，深人讨论了其内部机理和模型结构，
并对这一解决方案的实现关键作了详解，包括实时数据库方案的数据模型。
主要融合技术应用的概念模型和异质信息融合算法等。由此完成了对DDSEI
信息融合设计从系统模型到关键技术的全面解决方案。
Intelligent Transportation System (ITS), a developing conformation of transportation system in the information times, has been integrating a variety of advanced technologies, especially information technology. It has the profoundness and complexity that the evolutions of transportation system did not have ever before. Because ITS has particular service characters totally different from traditional conformations, that is, travelers by automobiles are often the drivers of the vehicles, intelligent vehicle (IV) and its applications are the core of ITS applications. And because of the human factor constraints due to IV drivers, the key to IV applications is the information management or operation based on the needs in travel and driver capacity, including information measurement, integration and fusion. Thus, it is of theoretical and practical significance to study systematically on the Wuli-Shili* of information phenomenon stemming from every layer of ITS.
Aimed at information phenomenon of ITS, the contents are arranged as "from overall to partition, and macrolaw illustration in combination with microalgorithm creation". This paper introduce nonequilibrium thermodynamics and the theory of dissipative structures into transportation fields, especially into ITS. It studies, expounds and proves the entropy variation laws and dissipative characters of transportation system, reveals the negative entropy, the essance of information phenomenon of ITS, and presents the dissipative mechanism of ITS. This paper studies further driving decision support environment of information (DDSEI) of intelligent vehicle, which is composed of all the elements, i.e. drivers, vehicles, roads and information. The concept of DDSEI is defined in this paper, the information structure and internal relations of it are analized, then the general system description about DDSEI is set up. After the four-stage model for information managing of drivers is studied, the
* Wuli means objective systems and the scientific knowledge resulted from the human beings' research and discover on the world; Shili means how to generate some methods based on real existence, in order to direct the human beings' practices for more efficiency, benefits and perfection. Wuli - Shili (WS) is cited from the concept of Wuli - Shili - Renli (WSR) that was put forward firstly by Prof. Gu Jifa, and it has received broad recognition and is being applied in many fields.
concept model for DDSEI designing is proposed.
This paper summarizes traditional concepts of human factors and the Information characters of intelligent vehicles, proposes the concept of human factor compound information and discusses its mostly characters. According to the complexity of human factors, this paper analizes the restricts of current algorithms, design an evaluation index architecture for driving on highways. Based on the diversion mechanism of information states in DDSEI and information acquisition process of drivers, this paper recommends to optimize the DDSEI designs with diversion information entropy and presents the formula of the entropy. And the adaptive mechanism of DDSEI is defined and its adaptive man-machine interface architecture is also established that is made up of user model, task model and interface model, then the study of the principles and functions about all the three models is carried out and the working flow of the adaptive interface is achieved.
According to the characters of the human factor compound information and the limits of the current working methods, this paper puts forward a series of defines focused on the concept of semantic tree, creats the concept of multidimentional semantic measurement embodying information depth measurement, information granulation measurement and information form measurement, and the relative algorithms or formulas are given out. At the same time the general function description of syntactic information measurement of DDSEI is also created, and the expectation pragmatic information measurement of a information source, and of DDSEI, pra
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