行人运动建模技术综述*

随着计算机数值模拟技术的迅速发展,行人运动仿真技术开始成为人群疏散仿真、城市规划和计算机疏散软件开发方面的研究热点。介绍了对行人建模仿真的初步研究,重点介绍了基于行人行为的微观行人运动建模方法,并对各个模型进行了比较分析和研究展望。     

行人运动建模研究综述EI北大核心CSCD

行人运动建模是近年来虚拟现实领域的热门研究方向,在游戏娱乐、公共安全以及智慧交通等领域有着重要的应用价值.文中按照规则驱动-数据驱动和宏观-微观2个维度将现有的行人运动建模方法划分为4大类.首先简述各类方法的典型模型,重点介绍各类方法的最新研究进展,分析了各类方法的特点;其次介绍行人运动建模工作中常用的数据集和评价指标,并从碰撞避免、运动多样性、场景迁移能力以及仿真精度等角度对各类方法进行评价;最后分析在数字孪生、元宇宙等新技术兴起背景下行人运动建模方法面临的挑战,指出该领域未来发展方向.     

基于势能场的机场行人行为建模仿真研究

研究机场人流优化疏散问题,由于机场内人流和滞留旅客量大,进行行人行为建模得出行人活动规律,是进行人员疏散、维护公共安全的基础.针对机场的空间特点,研究人员采用势能场方法对行人行为建模,通过目的地和障碍物等对行人的影响来确定行人的总势能.同时采用精细网格模型对特定区域进行行人行为仿真.仿真结果表明,在变截面空间的运动中,行人在低密度情况下可不受截面变化影响.对于高密度情况,行人会受到相互干扰,以被迫改变自己的运动轨迹.研究给出了相关行人轨迹,为疏散提供参考.     

航站楼枢纽行人微观寻径算法仿真研究

为了提高航站楼等交通枢纽内应急预案和疏散措施的科学性和有效性,需要对其内部行人的行为进行建模分析,从而得出行人活动和在枢纽设施内的寻径规律。研究人员采用邻域决策模型对行人行为建模,通过目的地、障碍物和其他行人对行人的交互影响作用来确定行人的轨迹,同时采用样例对该模型进行仿真。仿真实验结果表明,与传统的基于力的模型等微观行人仿真相比,该模型具有非线性的决策特征,从而有更好的微观仿真效果。     

基于涌现视角的MAS信任模型仿真分析方法

目前MAS信任模型研究关注的焦点是信任管理的微观机制设计,而从这些微观机制到系统的宏观特性,并不是显而易见的.因此,这些研究都采用了仿真分析.但是这些仿真实验的设计大多缺乏理论指导,往往不能正确反映系统的宏观特性.引入系统科学中的涌现视角来审视MAS,探讨基于涌现视角的MAS信任模型仿真分析方法.这一方法从相对清晰的微观层面入手,建立仿真运行模型;在充分考虑的相关宏观约束下,研究系统宏观层面的问题以及微观和宏观之间的联系.总结分析了信任模型微观机制的设计图式,在此基础上探讨了仿真Agent的设计框架;讨论了信任模型仿真分析中应考虑的宏观问题,包括宏观约束、威胁模型、评价指标以及仿真执行中的问题,最后实例验证了此方法的有效性.     

改进社会力的双向行人流模型研究

在行人运动和行人行为观察的基础上,结合社会力模型,把行人运动看作一种自驱动的个体在连续空间中移动的过程。为了解决行人间相互作用力的不足,通过引入行人运动感知域,提出一种行人碰撞避免的解决机制,这种机制能较好解决行人间的相互碰撞行为,防止行人间出现大面积的堵塞现象。仿真实验结果显示改进后的模型能再现正常条件下行人流基本图,并很好地再现了行人流“通道形成”自组织现象。     

基于元胞自动机的行人模型仿真研究

为了直观地显示行人行走的特性以及与车流之问的相互作用,在分析行人交通特性后,以元胞自动机(CA)和规则描述为理论基础,提出基于CA的行人仿真模型,利用VC建立相对于其他交通对象的行人避让模型,最后用OpenGL对模型进行可视化渲染.方法以模型与现实世界的高相似性有效克服了精确数学模型在仿真行人交通时的难题.结合行人cA模型和行人避让模型开发了混合交通流仿真试验原形系统.仿真结果表明,方法有效地模拟了现实交通状况,在城市交通规划、交通需求管理和交通控制等领域有广泛的应用前景.     

考虑个体行为的改进CA模型人员疏散研究

为更好模拟行人疏散过程中微观个体行为,考虑行人身材半径及在疏散过程中行人步行速度随运动状态变化,将社会力模型运行规则引入元胞自动机模型,建立了一种社会力模型计算步行速度、空间离散化程度和步行速度较高的疏散模型,用于模拟紧急情况下的行人疏散过程。在该模型中空间划分为更小网格,每个行人占用一到多个单元格,行人的身材半径不再不变,每个行人移动的距离由其速度决定,根据基于速度的出口选择方法和行人运动规律,通过数值模拟分析,研究了疏散过程中的动态性。研究表明基于速度的网格移动数量、行人数量、期望速度、行人身材半径、松弛时间等参数影响疏散效率,结合连续模型的优点能够更加客观真实刻画疏散过程,有助于离散模型描述行人疏散微观行为特征。     

基于关节轨迹规划对行人步态的仿真

为提高智能车辆的行人检测成功率,同时保证人身安全,提出一套完整的仿行人步态装置研发流程,通过建模仿真的方法促进该装置的研发并降低成本。仿真模型按照人体真实数据进行设计,步态的变化主要体现在四肢上;利用D-H参数法和正逆运动学知识,结合MATLAB建模和关节轨迹规划的方法,实现运动仿真;成功模拟出行人步态,得出仿真模型的末端轨迹、关节转角等步态指标在一个周期内的变化。将仿真结果与标准人类行走周期图谱进行了对比验证,保证了仿真模型的结构设计以及运动学模型的合理性,为后续的实际开发提供一定理论依据。     

复杂系统理论及其建模仿真方法学

论文介绍了系统理论的研究与发展,重点讨论了复杂适应系统理论及其建模仿真方法学,给出了有关复杂系统的一系列定义,研究了复杂适应系统微观模型结构和由微观到宏观的过渡方法,指出了仿真方法是分析研究复杂系统的唯一有效途径.     

Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice

Large environments that are designed for travel, leisure, and for everyday life – such as transport hubs, amusement parks, and shopping centers – feature different locations that are frequently visited by pedestrians. Each visit is evoked by one’s motivation to engage in some kind of activity at a certain location. By means of modeling the pedestrians’ interests in locations with the aid of computer simulations, it is possible to forecast the occupancy at locations by utilizing sophisticated pedestrian destination choice models. In the field of pedestrian dynamics research, location preference modeling is not common, but it is all the more rare to include a psychological grounding into such choice models. Here we show that our psychologically inspired and mathematically defined model to describe pedestrians’ interests in locations is able to improve the exactness of pedestrian destination choice models. The interest function model is based on the psychological concept of goal-related memory accessibility and on fundamental coherences found in pedestrian-related data that is measurable at locations. We validated the interest function model and our results provide evidence that our approach improves the simulation fidelity regarding occupancy forecasting. Because the interest concept is designed as a framework that can be coupled to existing microscopic pedestrian simulators, it can be used in most pedestrian destination choice models to describe pedestrian visiting preferences. Consequently, the reliability of the occupancy predictions of pedestrian simulations can be enhanced by integrating the interest function model into choices models.     

Pedestrian detection and tracking at crossroads

This paper presents a system that can perform pedestrian detection and tracking using vision-based techniques. A very important issue in the field of intelligent transportation system is to prevent pedestrians from being hit by vehicles. Recently, a great number of vision-based techniques have been proposed for this purpose. In this paper, we propose a vision-based method, which combines the use of a pedestrian model as well as the walking rhythm of pedestrians to detect and track walking pedestrians. Through integrating some spatial and temporal information grabbed by a vision system, we are able to develop a reliable system that can be used to prevent traffic accidents happened at crossroads. In addition, the proposed system can deal with the occlusion problem. Experimental results obtained by executing some real world cases have demonstrated that the proposed system is indeed superb.     

Modeling vehicle–pedestrian interactions outside of crosswalks

Pedestrian and driver behaviors as well as their interactions, are essential in planning, designing and operating highway facilities. Pedestrian crossing outside of a marked or unmarked crosswalk (i.e. jaywalking), is one of those pedestrian behaviors that may highly affect safety and operations. Unlike permissible crossings at crosswalks, jaywalking events are not often anticipated by drivers, which may result in less driver reaction time and different vehicle operation dynamics. It is important to understand pedestrian crossing behavior outside of crosswalks, as well as driver yielding behavior towards them. To date, limited quantitative and behavioral research has been conducted to investigate this interaction or simulate it microscopically. This paper aims to explore both pedestrian jaywalking behavior (gap acceptance and speeds) and the corresponding driver reactions (yielding behavior) for modeling the vehicle–pedestrian interactions (VPI) outside the crosswalks in a micro-simulation environment. The study also quantifies the differences between vehicle–jaywalker and vehicle-permissible crossing. An observational study and an instrumented vehicle study were conducted on the campus of the University of Florida to collect data from pedestrian and driver perspectives, respectively.Crossing speed, yield acceptance and delay of jaywalking crossings and permissible crossings were observed in the study and these attributed can be used for replicating pedestrian operations in simulators. Moreover, behaviors of driver approaching jaywalkers versus pedestrians crossing at designated crosswalks were compared on the basis of yield rates, and vehicle speed profiles. Vehicle yield dynamics were analyzed to model the driver reactions towards jaywalkers. Lastly, it was found that the locations of jaywalking events are highly concentrated and influenced by the crossing environment, such as pedestrian and vehicular volume, bus stops presence and crossing distance.This paper establishes several quantitative relationships describing interactions between pedestrians crossing outside of crosswalks and approaching drivers, which provide the basis and assumptions for modeling such interactions in a micro-simulation environment for traffic operational analyses.     

Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models

This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic behaviors without additional learning. This strategy allows us to evaluate the robustness and the consistency and quality of the learned behaviors. For this purpose several tools from pedestrian dynamics, such as fundamental diagrams and density maps, are used. The results reveal that the developed model is capable of simulating human-like micro and macro pedestrian behaviors for the simulation scenarios studied, including those where the number of pedestrians has been scaled by one order of magnitude with respect to the situation learned.     

Analysis of traffic hazard intensity: A spatial epidemiology case study of urban pedestrians

Traffic safety studies have underscored the hazardous conditions of pedestrians in the United States. This situation calls for increased public awareness of the pedestrian safety issue and better knowledge of the main factors contributing to traffic hazard for urban pedestrians. The purpose of this spatial epidemiology research is to gain greater insights into the geographic dimension exhibited by the intensity of traffic collisions involving urban pedestrians. Pedestrian crashes are studied in Buffalo, NY for years 2003 and 2004. Factors of hazard intensity are determined and compared for three age cohorts as well as for collisions occurring at intersections versus mid-block locations. Physical road characteristics and density of development, as well as socio-economic and demographic variables and potential trip attractors are examined. Spatial regression models are used to account for spatial dependencies. Econometric analysis underscores that all classes of environmental factors tested are significant drivers of pedestrian traffic hazard intensity. Results of the geographic analysis indicate that young and adult pedestrian traffic hazard intensities follow rather distinct logics. In addition, intersection and mid-block crashes differ by their socio-economic correlates, as well as their spatial distribution in the urban fabric.     

Reactive pedestrian path following from examples

Architectural and urban planning applications require animations of people to present an accurate and compelling view of a new environment. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper, we explore an approach for generating reactive path following based on the users examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using a motion-graph approach. We discuss the accuracy of the learned model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.     

统一对象模型—对GIS融入IT的认识

The process of manufacturing tools by human being has experienced three stages material tools,energy tools and information tools. Information tools compose information system in the form of systems. Information systems are mapping of entities and relationships between them in real world. It composes of objects and relationships,objects can be classified into Facility Objects and Domain Objects, Domain Objects are inheritance and extension of Software Facility Objects ,Software Facility Objects are generalization and abstraction of Domain Objects. Information systems are digital model of the real world; the core problems of IT development are modeling the real world. The development of GIS extends the current object model,to construct a Unified Object Model with spatial property. It makes IT modeling the real world more effectually,information is organized and analyzed in the spatial framework,it will improve the unification of spatial and temporal recognition in IT and promote the development of knowledge tools. Current GIS technology is suitable for resolving the macro spatial problems,Micro GIS must be developed to solve the micro spatial problem.     

基于CNN-ATT-ConvLSTM的行人属性识别

针对现有行人属性识别方法忽视行人属性的互相关性和空间信息导致识别性能较低的问题,将任务视为时空序列多标签图像分类问题,提出基于卷积神经网络(CNN)和卷积长短期记忆网络(ConvLSTM)并融合通道注意力机制的模型。用CNN和通道注意力提取行人属性的显著性和相关性视觉特征;用ConvLSTM进一步提取视觉特征的空间信息和属性相关性;以优化序列对行人属性进行预测。在两个常用行人属性数据集PETA和RAP上进行大量实验,取得了最佳性能,证明了该方法的优越性和有效性。     

基于特征定位与融合的行人重识别算法

行人外观属性是区分行人差异的重要语义信息。行人属性识别在智能视频监控中有着至关重要的作用,可以帮助我们对目标行人进行快速的筛选和检索。在行人重识别任务中,可以利用属性信息得到精细的特征表达,从而提升行人重识别的效果。文中尝试将行人属性识别与行人重识别相结合,寻找一种提高行人重识别性能的方法,进而提出了一种基于特征定位与融合的行人重识别框架。首先,利用多任务学习的方法将行人重识别与属性识别结合,通过修改卷积步长和使用双池化来提升网络模型的性能。其次,为了提高属性特征的表达能力,设计了基于注意力机制的平行空间通道注意力模块,它不仅可以在特征图上定位属性的空间位置,而且还可以有效地挖掘与属性关联度较高的通道特征,同时采用多组平行分支结构减小误差,进一步提高网络模型的性能。最后,利用卷积神经网络设计特征融合模块,将属性特征与行人身份特征进行有效融合,以获得更具鲁棒性和表达力的行人特征。实验在两个常用的行人重识别数据集DukeMTMC-reID和Market-1501上进行,结果表明,所提方法在现有的行人重识别方法中处于领先水平。