基于空间认知的智能导航方法研究
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摘要
口前,大多数导航系统采用以数据模型和数学计算为基础的数据驱动方法实现路径规划和引导。尽管能够从经济角度提高交通的效率,然而由于忽略人们对空间信息的获取、加工和推理模式,这种导航模式还存在着一些局限性。而基于认知概念方法的路径规划和引导正在成为导航领域的研究热点。
相关研究已经表明,将人们导航活动中的空间认知原理体现在导航系统的开发中,可以使导航过程更加符合人们找路和指路的认知习惯,进而能够增强导航系统的有效性,提高用户的自信心和满意度,降低用户的心理压力和精力分散,减少在陌生环境下导航的不确定性,并帮助用户构建环境的认知地图。
认知导航的研究主要具有三个特点：1)普遍考虑了地标对导航系统可用性的改善；2)在路径规划过程中考虑体现人们空间认知原理的最优路径选择标准；3)在路径引导过程中采用接近人们空间交际的方式组织和表达路径信息。尽管成果已经比较丰硕,已有研究中仍然存在以下三方面的不足：
1.难以简捷、高效地从环境中提取地标数据。目前比较有代表性地标提取方法(如空间对象显著性度量模型、空间数据挖掘方法、网络资源搜索方法等)通常需要采集、处理和维护海量数据(如空间对象的外观、语义、结构特征或描述文本等),实现起来比较困难。
2.空间认知导向的路径规划过于依赖具体的网络模型。由于不同用户通常具有不同的认知偏好,其关注的地标或各认知特征的权重系数通常有较大差异。因此,在将空间认知规则融入到路网的权值时,通常需要针对不同用户生成不同的路网模型。这会给路网数据的共享和维护带来较大麻烦。
3.难以将用户的先验空间知识无缝融合到基于转向的路径引导中。目前,基于转向的路径引导通常以路径上每个决策点为基本参考对象,而体现用户先验空间知识的路径引导通常参考到环境中有全局显著特征的空间要素。由于缺少统一表达两种参考对象的路径表达框架,这两种路径引导方式通常是独立或交叉进行的。
针对以上三方面的问题,本文提出了针对性的解决方法。以地标提取、路径规划和路径引导方法为核心,本文主要包含以下五个方面的内容：
1.首先提出本文的研究背景和意义。从以数据模型和数学计算为基础的传统导航系统到以基于认知概念方法的智能导航系统的演进,然后总结认知导航领域的国内外研究进展,包括路径规划、路径引导和路径信息具象化的算法和方法,并分析当前研究中存在的主要问题,进而提出本文的研究目标和主要内容。
2.总结本文研究内容的主要理论基础。具体表现在利用与人们的空间认知过程尤其是导航活动相关的空间认知本体和空间认知理论实现对环境的特征化。其中,被讨论的空间认知本体主要包括空间认知要素、定性空间关系以及空间参考系统等,涉及的空间认知理论主要包括空间组合、空间分层以及空间交际等。
3.提出一种基于城市兴趣点(points of interest, POI)数据的分层地标提取方法。该方法从公众认知、空间分布和个体特征三个方面综合衡量各POI对象的显著程度,讨论了利用问卷调查、多密度空间聚类和数据规格化方法计算POI对象各项显著性指标值的过程。最后,显著度较高的若干层对象被当作地标,其加权Voronoi图被用来反映各地标的空间影响范围及上下层关系。
4.提出一种不依赖完备路网模型的基于分层强化学习的自适应路径规划方法。在该方法中,道路网仅约束强化学习智能体的状态转移规律,影响路径选择的空间认知因素被转换为状态变化的即时奖励方程。本方法定义了符合人们空间认知习惯的路径选择标准,并采用基于网络Voronoi图的分层强化学习实现自适应路径规划。该方法分为两个阶段：预学习阶段自动发现子目标节点,并构建包含局部最优策略的子任务;实时学习阶段利用预定义策略实现高效的Q值更新,并根据Q值追溯最优路径。
5.提出一种面向自然语言且能够实现转向决策和先验知识无缝融合的自适应路径引导方法。该方法提出了一个将路径抽象为一系列结构统一、具有时序性和多粒度性、符合人们认知习惯又能反映用户空间知识、且可以被加工为指导用户沿路径前进的并易于转换为自然语言的短语或句子的指示单元的表达框架；并说明了利用环境结构、路径特征、先验知识等上下文因素生成多粒度的指示单元,从中选择最合适的指示单元序列,进而实现自适应路径引导的过程。
Most navigation systems today realize their functions of route selection and route directions with data-driven approaches, which are always designed upon explicit quantitative models and exact mathematical computations. Although they are considered to be effective to improve economic efficiency of commercial or fleet traffic, the neglect of human cognitive principles in spatial information acquiring, processing and reasoning may lead to some limitations of navigation services in this mode.
In recent years, route selection and directions on the basis of cognitive conceptual approaches have been becoming research focuses in the fields of navigation. Relevant studies shows that the integration of spatial cognition principles of human navigation activities into the development of navigation systems can make selected routes in line with the cognitive habits of human wayfinding, and make route directions close to the way of describing routes to others in direction giving. Hence, the effectiveness and acceptability of these navigation systems could be significantly enhanced, while user confidence and satisfaction being increased, user mental workload, attention distraction and navigational uncertainty being reduced. Besides, it is helpful for users to form a cognitive map of the environment in this case.
Researches on cognitive navigation have three major characteristics. First, landmarks are generally employed for improving the usability of navigation systems. Second, optimal route selection criteria reflecting the principles of human spatial cognition are frequently considered in the process of route selection. Third, the organization and representation of route information close to the way of human spatial communication are usually adopted in route directions. Although related achievements are abundant, existing researches are still insufficient in the following three aspects.
1. It is difficult to extract landmarks from the envrionment in a simple and efficient way, as the representative landmark extraction approaches at present, such as feature significance measuring, spatial data mining and web resources searching, usually need acquisition, processing and maintenance of massive data, such as the visual, structural and cognitive characteristics of spatial features and route descriptive textes.
2. The realization of spatial cognition motivated route selection is over-reliant on specific network models. As the diversity of user cognitive preferences leads to different concerned landmarks and different weight coefficients of cognitive criteria, personalized network models need be generated to meet different users when integrating regulations of spatial cognition into weights of road network. This could bring big troubles to the sharing and maintenance of road network data between different users.
3. It is hard to seamlessly integrate the a-priori environmental knowledge of users into turn-by-turn route directions. At present, the primary reference objects of turn-by-turn route directions are always the decision points on the route, while the route directions considering user's a-priori spatial knowledge are usually referred to environmental features with global salient characteristics. As the absence of a route representation framework which could uniformly represent these two kinds of reference objects, turn-by-turn route directions and knowledge based route directions are generally implemented individually or alternately.
Against to the above three issues, pertinent solutions are proposed in this thesis. Focused on the approaches of landmark extraction, route selection and route directions, this study mainly includes contents in the following five parts.
1. The research background and significance of this thesis are introduced and discussed. Firstly, the necessity of development from traditional model and computation driven navigation systems into cognitive conceptual approach based intelligent navigation systems is pointed out. Then, the research progresses of cognitive navigation, including route selection algorithms, route direction approaches and route information externalization, are reviewed. After the analysis of existing problems in literature, the research objectives and research contents of this thesis are made clear.
2. The theoretical foundations of the main research contents in this thesis are summarized. This is specifically shown in the processes of environmental aspectualization according to some cognitive ontologies and cognitive theories related to the human activities of spatial cognition especially navigation. The introduced spatial cognitive ontologies include cognitive conceptual primitives, qualitative spatial relations and spatial reference systems. Related spatial cognitive theories involve spatial chunking, spatial hierarchization and spatial communication.
3. An approach of extracting hierarchical landmarks from points of interest (POI) in urban environments is proposed. This approach measures the significance of every POI object from three factors, which are public cognition, spatial distribution and individual characteristic. Based on a significance measure model composed of three vectors corresponding to these three factors, the processes of computing the vector values of a POI are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively. At last, the POI objects with relative high significances are treated as landmarks in different levels. Then, the weighted Voronoi diagrams generated from each level of landmarks can reflect the influence area of every landmark and associate the landmarks in the same level and between the sequential levels.
4. A complete network model independent, interactive route selection approach using hierarchical reinforcement learning (HRL) is presented. In the learning process, the state transitions of the agent are constrained by the topological structure of streets, each perceived state-action pair of the environment is mapped into a immediate reward function of turning decisions at intersections. In this approach, the route selection criteria for cognitively motivated optimal routes are defined, and optimal route policies with maximal cumulative rewards can be adaptively found through a two-stage network Voronoi diagram based learning process. The first pre-learning stage automatically identifies some nodes in road network as subgoals and constructs corresponding subtasks containing local optimal route policies for achieving the subgoals. The second real-time learning stage focuses on efficiently updating the Q-values of every available state-action pair using predefined policies, and tracing the optimal routes after convergence. Furthermore, this approach can efficiently adapt to permanent or sudden environmental changes.
5. A context-adaptive route direction oriented to natural language is proposed for the seamless integration of user's a-priori spatial knowledge into turn-by-turn route directions. In this approach, a route representation framework and its main implementation procedures are proposed. In the framework, a route is represented as a sequence of uniform temporal and various granular instruction units, which could meet human cognitive habits, may reflect user's spatial knowledge, and can be processed into route instruction phrases or sentences which are apt to be expressed in natural language. For the implementation of context-adaptive route directions, landmark extraction, various granular instruction unit generation and most appropriate instruction unit sequence selection are introduced, while some contextual factors such as environmental structures, route characteristics and prior knowledge are also considered in these procedures.
相关研究已经表明,将人们导航活动中的空间认知原理体现在导航系统的开发中,可以使导航过程更加符合人们找路和指路的认知习惯,进而能够增强导航系统的有效性,提高用户的自信心和满意度,降低用户的心理压力和精力分散,减少在陌生环境下导航的不确定性,并帮助用户构建环境的认知地图。
认知导航的研究主要具有三个特点：1)普遍考虑了地标对导航系统可用性的改善；2)在路径规划过程中考虑体现人们空间认知原理的最优路径选择标准；3)在路径引导过程中采用接近人们空间交际的方式组织和表达路径信息。尽管成果已经比较丰硕,已有研究中仍然存在以下三方面的不足：
1.难以简捷、高效地从环境中提取地标数据。目前比较有代表性地标提取方法(如空间对象显著性度量模型、空间数据挖掘方法、网络资源搜索方法等)通常需要采集、处理和维护海量数据(如空间对象的外观、语义、结构特征或描述文本等),实现起来比较困难。
2.空间认知导向的路径规划过于依赖具体的网络模型。由于不同用户通常具有不同的认知偏好,其关注的地标或各认知特征的权重系数通常有较大差异。因此,在将空间认知规则融入到路网的权值时,通常需要针对不同用户生成不同的路网模型。这会给路网数据的共享和维护带来较大麻烦。
3.难以将用户的先验空间知识无缝融合到基于转向的路径引导中。目前,基于转向的路径引导通常以路径上每个决策点为基本参考对象,而体现用户先验空间知识的路径引导通常参考到环境中有全局显著特征的空间要素。由于缺少统一表达两种参考对象的路径表达框架,这两种路径引导方式通常是独立或交叉进行的。
针对以上三方面的问题,本文提出了针对性的解决方法。以地标提取、路径规划和路径引导方法为核心,本文主要包含以下五个方面的内容：
1.首先提出本文的研究背景和意义。从以数据模型和数学计算为基础的传统导航系统到以基于认知概念方法的智能导航系统的演进,然后总结认知导航领域的国内外研究进展,包括路径规划、路径引导和路径信息具象化的算法和方法,并分析当前研究中存在的主要问题,进而提出本文的研究目标和主要内容。
2.总结本文研究内容的主要理论基础。具体表现在利用与人们的空间认知过程尤其是导航活动相关的空间认知本体和空间认知理论实现对环境的特征化。其中,被讨论的空间认知本体主要包括空间认知要素、定性空间关系以及空间参考系统等,涉及的空间认知理论主要包括空间组合、空间分层以及空间交际等。
3.提出一种基于城市兴趣点(points of interest, POI)数据的分层地标提取方法。该方法从公众认知、空间分布和个体特征三个方面综合衡量各POI对象的显著程度,讨论了利用问卷调查、多密度空间聚类和数据规格化方法计算POI对象各项显著性指标值的过程。最后,显著度较高的若干层对象被当作地标,其加权Voronoi图被用来反映各地标的空间影响范围及上下层关系。
4.提出一种不依赖完备路网模型的基于分层强化学习的自适应路径规划方法。在该方法中,道路网仅约束强化学习智能体的状态转移规律,影响路径选择的空间认知因素被转换为状态变化的即时奖励方程。本方法定义了符合人们空间认知习惯的路径选择标准,并采用基于网络Voronoi图的分层强化学习实现自适应路径规划。该方法分为两个阶段：预学习阶段自动发现子目标节点,并构建包含局部最优策略的子任务;实时学习阶段利用预定义策略实现高效的Q值更新,并根据Q值追溯最优路径。
5.提出一种面向自然语言且能够实现转向决策和先验知识无缝融合的自适应路径引导方法。该方法提出了一个将路径抽象为一系列结构统一、具有时序性和多粒度性、符合人们认知习惯又能反映用户空间知识、且可以被加工为指导用户沿路径前进的并易于转换为自然语言的短语或句子的指示单元的表达框架；并说明了利用环境结构、路径特征、先验知识等上下文因素生成多粒度的指示单元,从中选择最合适的指示单元序列,进而实现自适应路径引导的过程。
Most navigation systems today realize their functions of route selection and route directions with data-driven approaches, which are always designed upon explicit quantitative models and exact mathematical computations. Although they are considered to be effective to improve economic efficiency of commercial or fleet traffic, the neglect of human cognitive principles in spatial information acquiring, processing and reasoning may lead to some limitations of navigation services in this mode.
In recent years, route selection and directions on the basis of cognitive conceptual approaches have been becoming research focuses in the fields of navigation. Relevant studies shows that the integration of spatial cognition principles of human navigation activities into the development of navigation systems can make selected routes in line with the cognitive habits of human wayfinding, and make route directions close to the way of describing routes to others in direction giving. Hence, the effectiveness and acceptability of these navigation systems could be significantly enhanced, while user confidence and satisfaction being increased, user mental workload, attention distraction and navigational uncertainty being reduced. Besides, it is helpful for users to form a cognitive map of the environment in this case.
Researches on cognitive navigation have three major characteristics. First, landmarks are generally employed for improving the usability of navigation systems. Second, optimal route selection criteria reflecting the principles of human spatial cognition are frequently considered in the process of route selection. Third, the organization and representation of route information close to the way of human spatial communication are usually adopted in route directions. Although related achievements are abundant, existing researches are still insufficient in the following three aspects.
1. It is difficult to extract landmarks from the envrionment in a simple and efficient way, as the representative landmark extraction approaches at present, such as feature significance measuring, spatial data mining and web resources searching, usually need acquisition, processing and maintenance of massive data, such as the visual, structural and cognitive characteristics of spatial features and route descriptive textes.
2. The realization of spatial cognition motivated route selection is over-reliant on specific network models. As the diversity of user cognitive preferences leads to different concerned landmarks and different weight coefficients of cognitive criteria, personalized network models need be generated to meet different users when integrating regulations of spatial cognition into weights of road network. This could bring big troubles to the sharing and maintenance of road network data between different users.
3. It is hard to seamlessly integrate the a-priori environmental knowledge of users into turn-by-turn route directions. At present, the primary reference objects of turn-by-turn route directions are always the decision points on the route, while the route directions considering user's a-priori spatial knowledge are usually referred to environmental features with global salient characteristics. As the absence of a route representation framework which could uniformly represent these two kinds of reference objects, turn-by-turn route directions and knowledge based route directions are generally implemented individually or alternately.
Against to the above three issues, pertinent solutions are proposed in this thesis. Focused on the approaches of landmark extraction, route selection and route directions, this study mainly includes contents in the following five parts.
1. The research background and significance of this thesis are introduced and discussed. Firstly, the necessity of development from traditional model and computation driven navigation systems into cognitive conceptual approach based intelligent navigation systems is pointed out. Then, the research progresses of cognitive navigation, including route selection algorithms, route direction approaches and route information externalization, are reviewed. After the analysis of existing problems in literature, the research objectives and research contents of this thesis are made clear.
2. The theoretical foundations of the main research contents in this thesis are summarized. This is specifically shown in the processes of environmental aspectualization according to some cognitive ontologies and cognitive theories related to the human activities of spatial cognition especially navigation. The introduced spatial cognitive ontologies include cognitive conceptual primitives, qualitative spatial relations and spatial reference systems. Related spatial cognitive theories involve spatial chunking, spatial hierarchization and spatial communication.
3. An approach of extracting hierarchical landmarks from points of interest (POI) in urban environments is proposed. This approach measures the significance of every POI object from three factors, which are public cognition, spatial distribution and individual characteristic. Based on a significance measure model composed of three vectors corresponding to these three factors, the processes of computing the vector values of a POI are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively. At last, the POI objects with relative high significances are treated as landmarks in different levels. Then, the weighted Voronoi diagrams generated from each level of landmarks can reflect the influence area of every landmark and associate the landmarks in the same level and between the sequential levels.
4. A complete network model independent, interactive route selection approach using hierarchical reinforcement learning (HRL) is presented. In the learning process, the state transitions of the agent are constrained by the topological structure of streets, each perceived state-action pair of the environment is mapped into a immediate reward function of turning decisions at intersections. In this approach, the route selection criteria for cognitively motivated optimal routes are defined, and optimal route policies with maximal cumulative rewards can be adaptively found through a two-stage network Voronoi diagram based learning process. The first pre-learning stage automatically identifies some nodes in road network as subgoals and constructs corresponding subtasks containing local optimal route policies for achieving the subgoals. The second real-time learning stage focuses on efficiently updating the Q-values of every available state-action pair using predefined policies, and tracing the optimal routes after convergence. Furthermore, this approach can efficiently adapt to permanent or sudden environmental changes.
5. A context-adaptive route direction oriented to natural language is proposed for the seamless integration of user's a-priori spatial knowledge into turn-by-turn route directions. In this approach, a route representation framework and its main implementation procedures are proposed. In the framework, a route is represented as a sequence of uniform temporal and various granular instruction units, which could meet human cognitive habits, may reflect user's spatial knowledge, and can be processed into route instruction phrases or sentences which are apt to be expressed in natural language. For the implementation of context-adaptive route directions, landmark extraction, various granular instruction unit generation and most appropriate instruction unit sequence selection are introduced, while some contextual factors such as environmental structures, route characteristics and prior knowledge are also considered in these procedures.
引文
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