CPS: Medium: Collaborative Research: Transforming Connected and Automated Transportation with Smart Networking, Cooperative Sensing, and Edge Computing

NSF Cyber-Physical Systems（CPS）资助将通过在网络、传感和计算这三个关键领域的创新以及它们之间的协同作用，推动互联和自动驾驶汽车（CAV）系统的发展。这项工作利用了几个有望改变地面交通系统的新兴技术趋势：更高速的无线连接，改进的车载和基于基础设施的传感能力，以及机器学习算法的进步。到目前为止，大多数相关的研究和开发都集中在个别技术上，导致效益有限。该项目将开发一个集成平台，通过解决与CAV运行条件相关的关键挑战，联合处理网络，传感和计算：例如，安全关键、高移动性、稀缺的机载计算资源、波动的网络条件、有限的传感器功能。研究团队将研究如何使用集成平台实现真实世界的CAV应用，例如提高公共服务人员的安全性，缓解瓶颈地区的拥堵，以及保护弱势道路使用者（VRU）。鉴于其跨学科性质，该项目将在多个研究领域产生广泛的影响，包括交通工程，移动的/边缘计算和机器学习。这项研究的结果将使CAV生态系统中的多个利益相关者受益：驾驶员、行人、CAV制造商、交通政府机构、移动的网络运营商等，最终提高国家交通系统的安全性和机动性。该项目还将为开展各种教育和外联活动提供一个平台。本研究的知识核心包括对地面交通CPS领域的几个基础性贡献。首先，它通过战略性地聚合4G/5G/WiFi/DSRC技术来创新车联网（V2X）通信，以提高网络性能。其次，它开发了一个合作的传感和感知框架，附近的车辆可以与边缘节点共享原始传感器数据，以创建一个全局视图，这可以提供扩展的感知范围和被遮挡物体的检测。关键的技术贡献是确保良好的可扩展性-允许许多移动车辆在网络资源有限且波动的情况下有效地共享数据。第三，它可以跨车辆和基础设施进行分区计算，以满足CAV应用的实时要求。第四，整合上述网络、传感和计算的构建模块，研究团队将开发一个优化框架，该框架可以对需要在何处以何种质量执行何种计算做出自适应的、资源感知的决策，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。

This NSF Cyber-Physical Systems (CPS) grant will advance the state-of-the-art of Connected and Automated Vehicle (CAV) systems by innovating in the three key areas of networking, sensing, and computation, as well as the synergy among them. This work leverages several emerging technology trends that are expected to transform the ground transportation system: much higher-speed wireless connectivity, improved on-vehicle and infrastructure based sensing capabilities, and advances in machine learning algorithms. So far, most related research and development focused on individual technologies, leading to limited benefits. This project will develop an integrated platform that jointly handles networking, sensing, and computation, by addressing key challenges associated with the operating conditions of the CAVs: e.g., safety-critical, high mobility, scarce on-board computing resources, fluctuating network conditions, limited sensor capabilities. The research team will study how to use the integrated platform to enable real-world CAV applications, such as enhancement of public service personnel's safety, alleviation of congestion at bottleneck areas, and protection of vulnerable road users (VRUs). Given its interdisciplinary nature, this project will yield a broad impact in multiple research communities including transportation engineering, mobile/edge computing, and machine learning. The outcome of this research will benefit multiple stakeholders in the CAV ecosystem: drivers, pedestrians, CAV manufacturers, transportation government agencies, mobile network carriers, etc., ultimately improving the safety and mobility of the nation's transportation system. This project will also provide a platform to conduct various education and outreach activities. The intellectual core of this research consists of several foundational contributions to the ground transportation CPS domain. First, it innovates vehicle-to-everything (V2X) communications through strategically aggregating 4G/5G/WiFi/DSRC technologies to enhance network performance. Second, it develops a cooperative sensing and perception framework where nearby vehicles can share raw sensor data with an edge node to create a global view, which can provide extended perceptual range and detection of occluded objects. The key technical contribution is to ensure good scalability - allowing many moving vehicles to efficiently share their data despite limited, fluctuating network resources. Third, it enables partitioning computation across vehicles and the infrastructure to meet the real-time requirements of CAV applications. Fourth, integrating the above building blocks of networking, sensing, and computation, the research team will develop an optimization framework that makes adaptive, resource-aware decisions on what computation needs to be performed where at which quality, to maximize the service quality of CAV applications.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.