The goal of this project is improved situation awareness for autonomous vehicles across many different networks. The approach is new theory and abstractions for systems where potentially moving physical systems join and leave the network at a high rate. Making these kinds of cyber-physical systems (CPS) efficient and safe requires leveraging the sensor information from other proximate vehicles over the network: this will enable vehicles to have much higher situational awareness--effectively seeing around corners. However, computation must be performed fast enough to accurately control the physical system, and coordination over networks makes this even more challenging. The research program is paired with an educational initiative integrated into the extensive mentoring program of the researchers, with an emphasis on involving students of diverse backgrounds. This project investigates CPSEdge, a software platform deployed at the network "edge", which aggregates sensor information from nearby vehicles, and intelligently shares resulting plans of action. CPSEdge leverages its network proximity to vehicles, and is carefully designed to reply to vehicles fast enough to keep up with a quickly changing physical environment. The tools and techniques developed for CPSEdge will offer greater situational awareness to autonomous vehicles, and improve the responsiveness, reliability, and security of the software platforms that manage them. CPSEdge is built on a new process abstraction that is lightweight and can scale up to very large systems, even under significant churn, while providing increased reliability and security. This abstraction is managed by the CPSEdge system to ensure that the requisite computation is conducted in real-time with the physical system. Sensor data will be fused to generate a probabilistic model of the environment, providing global planning for nearby vehicles.