EAGER: SAI: Cognitive Models of Human Social Wayfinding for the Redesign of Public Spaces

EAGER：SAI：用于公共空间重新设计的人类社会寻路认知模型

• 批准号: 2122119
• 负责人: Jacob Feldman
• 金额: $ 29.99万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122119&HistoricalAwards=false
• 关键词: EAGER; SAI; Cognitive; Models; Human

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.In 2020, many public spaces were hastily redesigned to optimize pedestrian flow in order to minimize the spread of COVID-19. Unfortunately, conventional methods for simulating how people move through public spaces do not take into account social factors that affect how people actually navigate in the presence of other people (social wayfinding). For example, these methods do not incorporate how people adjust to avoid others’ personal space, navigate around slower-moving people, or follow instructions from other people. Even worse, existing simulations usually assume everybody has identical abilities, which is rarely true in real populations. The goal of this project is to develop a system for simulating the flow of people through public spaces, including social aspects of human navigation, and incorporating people with a variety of abilities and disabilities. These more realistic simulations will be used to develop novel metrics and protocols for evaluating public spaces, which more thoroughly reflect the rich social behavior of real people. This project develops a new framework for modeling the flow of people through public spaces, called the Social Wayfinding-Inspired InFrasTructure (SWIIFT) design framework. The framework has three interlocking parts: human subjects experiments on human wayfinding, computational simulations of the flow of people through public spaces, and evaluation metrics for assessing design and re-design of real public spaces. In a series of experiments, human subjects will be immersed via Virtual Reality headsets into simulated spaces. These spaces will contain different numbers of simulated people, including people with variations in mobility (using wheelchairs, canes or walkers; pushing strollers; carrying heavy bags), sensory ability (e.g., visual impairments, hearing impairments), knowledge, and attention. Human subjects will receive different cues about which way to go, including visible pathways, signage, and verbal instructions. Data about the choices they make as they navigate through the virtual spaces will be incorporated into simulations, allowing us to develop realistic models of how people move through spaces under natural conditions. Finally, this framework will use these simulation models to evaluate potential modifications to real spaces, allowing potentially expensive changes to be accurately evaluated before they are carried out. The ultimate goal of this work is to enable public spaces to be made more efficient and more accessible for everyone, regardless of ability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

加强美国基础设施（SAI）是美国国家科学基金会的一个项目，旨在促进以人为本的基础研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门的创新，发展经济，创造就业机会，使公共部门提供的服务更有效率，加强社区，促进机会平等，保护自然环境，加强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化过程的知识如何使有效基础设施的建设和维护成为可能，从而改善生活和社会，并以技术和工程的进步为基础。2020年，许多公共空间被匆忙重新设计，以优化行人流量，以尽量减少COVID-19的传播。不幸的是，传统的模拟人们如何在公共空间中移动的方法并没有考虑到影响人们在其他人面前如何导航的社会因素（社会寻路）。例如，这些方法没有考虑到人们如何调整以避开他人的私人空间，如何绕过移动较慢的人，或者如何听从他人的指示。更糟糕的是，现有的模拟通常假设每个人都有相同的能力，这在实际人群中很少是正确的。这个项目的目标是开发一个系统来模拟人们通过公共空间的流动，包括人类导航的社会方面，并将各种能力和残疾的人纳入其中。这些更真实的模拟将用于开发评估公共空间的新指标和协议，这些指标和协议将更彻底地反映真实人群的丰富社会行为。该项目开发了一个新的框架来模拟公共空间的人流，称为社会寻路启发基础设施（swift）设计框架。该框架有三个相互关联的部分：人类寻路实验，公共空间人流的计算模拟，以及评估真实公共空间设计和重新设计的评估指标。在一系列实验中，人类受试者将通过虚拟现实耳机沉浸在模拟空间中。这些空间将容纳不同数量的模拟人，包括行动能力不同的人（使用轮椅、手杖或助行器；推着婴儿车；提着沉重的袋子）、感官能力（如视力障碍、听力障碍）、知识和注意力。人类受试者将收到不同的线索，包括可见的路径、标志和口头指示。人们在虚拟空间中穿行时所做选择的数据将被整合到模拟中，使我们能够开发出人们在自然条件下如何在空间中移动的现实模型。最后，该框架将使用这些模拟模型来评估对真实空间的潜在修改，允许在执行之前准确评估潜在的昂贵更改。这项工作的最终目标是使公共空间变得更高效，更适合每个人，无论能力如何。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents

• DOI: 10.1145/3581641.3584045
• 发表时间: 2023-03
• 期刊: Proceedings of the 28th International Conference on Intelligent User Interfaces
• 作者: Che-Jui Chang;Samuel S. Sohn;Sen Zhang;R. Jayashankar;Muhammad Usman;M. Kapadia

COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality

• DOI: 10.1007/978-3-031-19833-5_15
• 发表时间: 2021-12
• 作者: Honglu Zhou;Asim Kadav;Aviv Shamsian;Shijie Geng;Farley Lai;Long Zhao;Tingxi Liu;M. Kapadia

Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction

利用傅里叶等量线和测地线相互作用进行长期人群流量预测

• DOI: 10.24963/ijcai.2022/185
• 发表时间: 2022
• 期刊: Thirty-First International Joint Conference on Artificial Intelligence (IJCAI
• 作者: Sohn, Samuel S.;Moon, Seonghyeon;Zhou, Honglu;Lee, Mihee;Yoon, Sejong;Pavlovic, Vladimir;Kapadia, Mubbasir
• 通讯作者: Kapadia, Mubbasir

HM: Hybrid Masking for Few-Shot Segmentation

• DOI: 10.1007/978-3-031-20044-1_29
• 发表时间: 2022-03
• 作者: Seonghyeon Moon;Samuel S. Sohn;Honglu Zhou;Sejong Yoon;V. Pavlovic;Muhammad Haris Khan;M. Kapadia

The IVI Lab entry to the GENEA Challenge 2022 – A Tacotron2 Based Method for Co-Speech Gesture Generation With Locality-Constraint Attention Mechanism

IVI 实验室参加 2022 年 GENEA 挑战赛 — 基于 Tacotron2 的具有局部约束注意机制的共同语音手势生成方法

• DOI: 10.1145/3536221.3558060
• 发表时间: 2022
• 期刊: GENEA Challenge 2022
• 作者: Chang, Che-Jui;Zhang, Sen;Kapadia, Mubbasir
• 通讯作者: Kapadia, Mubbasir