Integrated Networking, Edge System and AI Support for Resilient and Safety-Critical Tele-Operations of Autonomous Vehicles

Project summary:

Autonomous vehicles (AVs) with an in-vehicle human safety driver have been tested on public roads for years, and several companies are now offering "robotaxi" trial services in selected US cities. Safety is of the utmost importance in transportation, as mistakes can be expensive, dangerous, or fatal. News stories about robotaxis creating havoc on the streets highlight the challenges posed by complex real-world traffic environments. Clearly, AVs with fully autonomous driving still have a long way to go, in spite of rapid advances in artificial intelligence (AI) and machine learning (ML). AV tele-operations are suggested as an alternative approach, wherein a human operator remotely controls an AV, perhaps only partially as the need arises. This notion is inspired by the potential offered by emerging fifth-generation (5G) networks. However, as of now, 5G for AV tele-operations remains more aspirational, as many challenges remain. The goal of this project is to tackle the challenges in supporting partial AV tele-operations over 5G and next-generation networks. This project helps facilitate safe and incremental adoption of tele-operated AVs to address societal challenges, while accelerating AV technology towards full autonomy. In particular, it provides a unique opportunity for testing AV tele-operations in Midwest winter and other scenarios. The project also serves as a forum for academia-government-industry collaboration and technology translation, and as a nexus point for broadening participation in research, education, and community outreach.

This interdisciplinary and transformative research agenda develops integrated networking, systems, and AI support for AV tele-operations. Key innovations include: 1) a semantics-oriented and fine-grained networking framework that exploits diversity to provide high bandwidth and low latency; 2) an agile, secure-by-design edge systems architecture that is optimized for AI workloads; 3) a novel application-driven, cross-layer, and whole-system approach that enables cooperation across end devices, networks, edge systems, and human operators; 4) an "AI-native" paradigm that systematically integrates AI/ML algorithms across layers and system components, with built-in mechanisms to mitigate the risks of inaccurate or false AI predictions; and finally, 5) a human-centered approach that combines faster-than-real-time AI simulations and integrated machine and human intelligence to seamlessly support human-in/on-the-loop. These innovations are incorporated into a prototype AV tele-operation platform called NextMOVE that provides resilient, safety-critical support for partially tele-operated AVs. The innovations broadly apply to other Industry 4.0 use cases, including smart manufacturing, precision agriculture and tele-health, which are vital to national prosperity, security, and well-being.