Zhi-Li Zhang leads successful test drive for $7M autonomous vehicles project
Under the leadership of Department of Computer Science & Engineering Professor Zhi-Li Zhang, the DRIVE-SAFE project recently completed it's first year of funding with a successful test drive at the University of Utah this July. This $7 million project aims to improve fifth-generation (5G) networks in order to enable autonomous vehicle (AV) teleoperations as part of the National Science Foundation’s (NSF) Breaking the Low Latency Barrier for Verticals in Next-G Wireless Networks (NSF Breaking Low) initiative.
A joint venture between the University of Minnesota (UMN), University of Utah (Utah), and University of California Riverside (UCR), with industry partners General Motors (GM) and Nokia, the DRIVE-SAFE project is one of three projects funded by the NSF Breaking Low Ideas Lab. The research will focus on enhancing the innovative vertical-aware framework to optimize both 5G networks and the vertical application that will allow for seamless remote operations of self-driving cars.
Self-driving cars and “robotaxis” need detailed mappings of the real-world environments they operate in and require massive data for extensive training and testing before they can be deployed. Even with this data, and despite their increasing sophistication, these vehicles can can get themselves into tricky situations that require a human driver to fix. This can present compounding traffic problems if there’s no qualified driver near the vehicle. But with all of the sensors and connectivity in these vehicles, why can’t a remote driver take control of a vehicle from afar?
The issue is latency — the time it takes for information to get back and forth between driver and vehicle. Anything less than instantaneous feedback is prohibitively dangerous when operating a two-ton machine, and current cellular networks are not up to the task.
Zhang's team is working on how remote driving and self-driving can complement each other. By solving the latency problem, remote drivers at teleoperation centers up to hundreds of miles away could take over in situations in which self-driving vehicles get stuck, lost, or confused. They could also operate vehicles in remote areas that lack the detailed map data self-driving modes rely on.
“So many of these types of projects end up in the ‘valley of death’ — great preliminary findings but no ability to turn them into results,” Zhang said. “We’re committed to making teleoperation feasible in the real world.”
The July demonstration utilized UMN's connected and automated vehicle (MnCAV), which has played a critical role in advancing research through UMN's MnCAV Ecosystem program. The UMN team modified it to be remote-drivable for this demonstration where the CAV made several laps of a parking lot on the University of Utah campus, all while being piloted from inside a conference room in the Merrill Engineering Building. Rather than using a commercial 5G network to send information back and forth to the vehicle, the teleoperation station connected to the vehicle via an OCUDU-enabled 5G network instantiated in the POWDER testbed and enhanced with network technologies developed by the DRIVE-SAFE team.
The researchers will demonstrate the DRIVE-SAFE capabilities again in October, kicking off the second year of their project. Plans for the second year include working with the other DRIVE-SAFE partner institutions, the University of California, Riverside, and GM to explore the feasibility of combining 5G and vehicle-to-everything capabilities to further enhance teleoperated driving.