Collaborative Research: PPoSS: Planning: Scaling Autonomous Vehicle Systems at the Edge: from On-Board Processing to Cloud Infrastructure

The focus of this project is on Connected and Autonomous Vehicles (CAVs) and the smart city infrastructure supporting their operation. Current CAV and smart city infrastructure systems do not scale well with the increasing number of applications and are overwhelmed with massive amounts of data collected from embedded, roadside, and edge devices. As more infrastructure and vehicle sensors begin to collect data, novel techniques and methodologies are required to improve the scalability of these systems.

The project’s novelties are developments of principles, abstractions, and methodologies for the design and implementation of scalable systems for CAVs and the smart city infrastructure supporting the operation of CAVs. The project's impacts are in the development and deployment of CAVs which will lead to a safer, cleaner, and more efficient transportation.

This project develops: (1) theoretical models, frameworks, and software libraries to support the design and implementation of scalable parallel algorithms on heterogeneous CAV platforms; (2) a highly-scalable system for opportunistic offloading of CAV applications to the cloud/edge that will perform on-CAV mixed-criticality scheduling and task-offloading selection, edge-performance-aware vehicle path planning, and multi-hop secure and private offloading; (3) a scalable and secure real-time collaborative detection system in which CAVs leverage sensing data from their on-board sensors and neighboring vehicles; (4) scalable, adaptive traffic-signal and CAV-trajectory coordination protocols via fine-grained sensing of traffic data while addressing the increased computation demands of increased volumes of data; and (5) a programming framework, including libraries and interfaces, that facilitates the development of scalable applications for CAVs and their supporting smart city infrastructure.

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.