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

这个项目与北卡罗来纳州夏洛特大学合作，与夏洛特梅克伦堡县解决行人安全和社区警务的社区挑战，在网络物理系统（CPS）的进步的基础上。 随着社区采用基于视觉的交通摄像头和十字路口智能交通标志等技术，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，因为个人和公共安全风险或建议当地警方响应。 这些技术可以提供更准确的全社区业务情况。 有了这个数据社区可以有一个更好的了解自己，并在既定的法律和习俗将更好地服务和保护个人和广大公众。 该规划补助金将使社区规划师、地方政府和企业沿着与技术专家、城市规划师和交通工程师一起探索这些新兴技术在改善社区生活质量方面的潜力。该规划补助金将利用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、边缘计算以及通常的CPS和物联网技术。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，并在城市的整个边缘网络上进行协作处理。该项目将推进行人和司机行为和模型的知识，特别是在城市交通环境中。它将使驾驶员行为的研究和表征驾驶员在环交通控制系统。计划中的广泛社区参与将有助于确定社区的目标和关注点，特别是在未来社区部署这些拟议技术时有关隐私和交通流动性的规划。

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.