A vision-based model of locomotion in crowded environments

拥挤环境中基于视觉的运动模型

• 批准号: 10372021
• 负责人: WILLIAM H WARREN
• 金额: $ 49.89万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10372021
• 关键词: 3-Dimensional; Activities of Daily Living; Behavior; Behavioral; Biology; Clinical; Code; Complex; Computers; Contrast Sensitivity; Crowding; Cues; Data; Device or Instrument Development; Environment; Evaluation; Experimental Models; Face; Frequencies; Grouping; Human; Image; Individual; Injury; Knowledge; Laws; Locomotion; Measures; Mediation; Methodology; Modeling; Motion; Optics; Outcome; Participant; Pattern; Peripheral; Persons; Physics; Positioning Attribute; Quality of life; Reporting; Research; Research Personnel; Resolution; Risk; Robotics; Sampling; Scheme; Self-Help Devices; Tactile; Testing; Time; Training; Transportation; Travel; Vision; Visual; Visual Fields; Visual impairment; Visual system structure; Visually Impaired Persons; Walking; Wheelchairs; animation; base; blind; data modeling; design; experimental study; head mounted display; improved; improved mobility; locomotor control; mobility aid; object motion; optic flow; sensory substitution; simulation; virtual; virtual environment; virtual reality environment; visual control; visual information; walking speed

People face complex mobility challenges in natural settings every day, when walking down a busy sidewalk, through a crowded train station, or in a shopping mall. To guide locomotion, the visual system detects information about self-motion through an evolving layout of objects and other pedestrians, and generates a safe and efficient path of travel. Individuals with low vision report mobility as one of the most difficult activities of daily living, particularly walking in crowds or using public transportation, with increased risks of collision, injury, and reduced independence. As yet, however, researchers do not understand how vision is used to control locomotor behavior in such complex, everyday settings. The long-term objective of the proposed project is to develop the first vision-based model of pedestrian behavior in dynamic, crowded environments, and use the results to design more effective assistive technology. Most models of locomotor control (from robotics, computer animation, and biology) assume the 3D positions and velocities of environmental objects as input, and plan a collision-free path according to objective criteria. A vision-based model would take the optical information available to a pedestrian and generate human-like paths of locomotion, based on experimental data. The first specific aim is thus to determine the effective visual information that guides walking with a crowd. Specifically, we will test the hypotheses that (a) optic flow, (b) segmented 2D motion, or (c) perceived 3D motion, is used follow multiple neighbors, and how this information is spatially and temporally integrated. The second specific aim is to determine the visual control laws that regulate walking speed and direction in a crowd. Specifically, we will test competing models of collision avoidance, following, and overtaking, and formalize a vision-based pedestrian model. Based on these results, the third specific aim is to evaluate alternative approaches to sensory substitution for locomotor guidance. Specifically, we will compare coding schemes for a vibrotactile belt based on recoding the effective optical variables in tactile patterns, or using the vision-based model to steer the user with directional cuing. Behavioral experiments will test the optical variables and control laws that govern locomotion in crowds, by manipulating visual displays during walking in an immersive virtual environment (12m x 14m). Agent-based simulations will compare competing models of the experimental data and previously collected crowd data. This methodology will enable us to test alternative hypotheses about visual information and visual control laws, and create an experimentally-grounded vision-based pedestrian model. Sensory substitution experiments will test normally-sighted participants in matched visual and tactile virtual environments; if the results are promising, tests with low-vision and blind participants will be pursued in subsequent applications. The research will contribute to basic knowledge about visually-guided locomotion in complex, dynamic environments, and apply it to the design of an assistive mobility device.

人们每天在自然环境中面临着复杂的移动挑战，当走在忙碌的人行道上时， 穿过拥挤的火车站，或者在购物中心。为了引导运动，视觉系统通过物体和其他行人的不断变化的布局来检测关于自我运动的信息，并生成安全的 高效的旅行方式。视力低下的人报告说，行动是他们最困难的活动之一。 日常生活，特别是在人群中行走或使用公共交通工具，碰撞风险增加， 伤害，减少独立性。然而，到目前为止，研究人员还不明白在如此复杂的日常环境中，视觉是如何被用来控制运动行为的。 该项目的长期目标是开发第一个基于视觉的动态拥挤环境中行人行为模型，并利用其结果设计更有效的辅助技术。 运动控制的大多数模型（来自机器人学、计算机动画和生物学）假定3D位置 和环境物体的速度作为输入，并根据客观标准规划无碰撞路径。一 基于视觉的模型将利用行人可用的光学信息， 运动的路径，根据实验数据。因此，第一个具体目标是确定有效的视觉 引导人们与人群同行的信息。具体来说，我们将测试以下假设：（a）光流，（B）分段的2D运动，或（c）感知的3D运动，被用于跟随多个邻居，以及这些信息是如何 空间和时间上的整合。第二个具体目标是确定视觉控制法， 在人群中行走的速度和方向。具体来说，我们将测试碰撞避免，跟随和超车的竞争模型，并正式确定基于视觉的行人模型。根据这些结果，第三个具体 目的是评估替代方法的感觉替代运动指导。具体来说，我们将比较编码方案的振动触觉带的基础上重新编码的有效光学变量的触觉模式， 或者使用基于视觉的模型来通过方向提示来引导用户。 行为实验将测试控制群体运动的光学变量和控制定律， 在沉浸式虚拟环境（12 m x 14 m）中行走时操纵视觉显示。基于agent 模拟将比较实验数据和先前收集的人群数据的竞争模型。 这种方法将使我们能够测试有关视觉信息和视觉控制的替代假设 法律，并创建一个实验接地视觉为基础的行人模型。感官替代实验将在匹配的视觉和触觉虚拟环境中测试视力正常的参与者;如果结果 虽然这项研究很有希望，但在随后的应用中将继续对低视力和盲人参与者进行测试。的 研究将有助于获得关于在复杂动态环境中视觉引导移动的基本知识，并将其应用于辅助移动设备的设计。