SCC-Planning: Pedestrian Safe and Secure Communities with Ambient Machine Vision

该项目由北卡罗来纳大学夏洛特分校与夏洛特-梅克伦堡县合作，以网络物理系统（CPS）的进步为基础，解决了行人安全和社区警务方面的社区挑战。随着社区采用诸如基于视觉的交通摄像头和十字路口的智能交通标志等技术，来自这些技术的数据拥有社区内活动的痕迹，其中一些可能需要响应，因为对个人和公共安全构成风险，或者建议当地警察响应。这些技术可以提供更准确的全社区运作情况。有了这些数据，社区可以更好地了解自己，并在既定的法律和习俗中更好地服务和保护个人和公众。这项规划拨款将使社区规划者、地方政府、企业以及技术专家、城市规划者和交通工程师能够探索这些新兴技术的潜力，以提高社区的生活质量。该规划拨款将利用CPS、大数据和城市交通规划方面的研究，为社区参与提供新的能力。它将利用计算机视觉、机器学习、边缘计算以及一般的CPS和物联网等技术。这将为设计用于城市街道交叉口环境视觉处理的边缘计算系统奠定基础，并在城市整个边缘网络上进行协同处理。该项目将促进对行人和驾驶员行为和模型的了解，特别是在城市交通环境中。这将为驾驶员在环交通控制系统中驾驶员行为的研究和表征提供可能。计划中的广泛社区参与将有助于确定社区目标和关注的问题，特别是在未来社区部署这些拟议技术的隐私和交通出行规划方面。

This project with the University of North Carolina at Charlotte in cooperation with the Charlotte-Mecklenburg counties addresses community challenges of pedestrian safety and community policing, building on advances in cyber-physical systems (CPS). As communities adopt technologies such as vision-based traffic cameras and smart traffic signs at intersections, the data from these technologies possess traces of the activity within a community of which a few might need a response because of risk to individual and public safety or suggest a local police response. Such technologies may provide a more accurate community-wide operational picture. With this data communities can have a better understanding of itself and within established law and custom will better serve and protect individuals and the public at large. This planning grant will enable community planners, local government, and businesses along with technologists, urban planners and traffic engineers to explore the potential of these emerging technologies for improving the quality of life of a community.This planning grant will leverage research in CPS, big data, and urban transportation planning to provide new capabilities for community engagement. It will draw upon technologies from computer vision, machine learning, edge computing, and generally CPS and the Internet of Things. This will set the stage for designing edge computing systems for ambient vision processing at city street intersections with cooperative processing over the entire edge network in a city. The project will advance knowledge of pedestrian and driver behaviors and models, specifically in urban transportation settings. It will enable the study and characterization of driver behaviors for driver-in-the-loop traffic control system. The planned extensive community engagement will facilitate ascertaining community goals and concerns, especially regarding privacy and transportation mobility planning in future community deployment of these proposed technologies.