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.