Intelligent Transportation Systems Research at MnRI
MnRI faculty and researchers are participating in a number of transportation projects involving connected vehicles, autonomous operation, intelligent warning systems, and robotics. These projects include research on autonomous driving (using the new MnCAV vehicle purchased by UMN’s Center for Transportation Studies), teleoperation research for robots and vehicles, snowplow guidance systems to assist operators during snow removal, development of automatic monitoring and alerting systems for construction zones, ramp metering control for a mixed-autonomy future, and development of an autonomous robot for mobile asphalt density profiling on roads.
In February the university received exciting news about the expansion and renewal of the Regional University Transportation Center CCAT (Center for Connected and Automated Transportation), with the USDOT announcing the center’s funding. CCAT is led by the University of Michigan and PI Prof. Henry Liu. UMN is a member of this expanded regional transportation center, and will receive $350,000 in federal funds during the first year (with an additional 1:1 match being provided by various sources, including MnDOT projects, MnRI, the College of Science and Engineering, and the Office of the Vice President for Research). In subsequent years, UMN will receive between $250,000 and $500,000 annually. Faculty involved in the new center include Profs. Zhi-Li Zhang (PI), Rajesh Rajamani, Nikos Papanikolopoulos, and Raphael Stern. UMN’s Center for Transportation Studies played a key role in supporting the preparation and submission of this proposal.
Dr. Nichole Morris is leading a project to analyze pedestrian safety and driver behavior near automated vehicles, focusing on when the vehicle is coming to a stop at traffic intersections. The project, funded by MnDOT, is currently collecting in-field data in Rochester, MN, where the Med City Mover project operates two low-speed, automated shuttles. This study will bolster general understanding of how drivers interact with automated vehicles—particularly large, slower-moving shuttles—and improve safety for drivers, pedestrians, and shuttle passengers.
Professors Michael Levin and Zongxuan Sun are exploring the use of connected vehicle technology to warn drivers if they are about to run a red light. In a project funded by the Local Road Research Board, they are developing a red-light-running warning system, which they will demonstrate on the TH-55 connected corridor. They will use dedicated short-range communications receivers with GPS chips to receive Signal Phase and Timing (SPaT) information from the traffic intersection. This information will be processed in real time on a laptop computer, with warnings displayed when appropriate. An appropriately timed warning could encourage drivers to brake before they enter the intersection unsafely.
Minnesota is home to one of the largest deployments of ramp meters in the country. Prof. Raphael Stern is leading a project, funded by MnDOT, to analyze how ramp metering strategies may be influenced by different vehicle automation scenarios, such as automated vehicle market penetration rates for futuristic fully automated vehicles and for currently available low-level automated vehicles. This project will also develop strategies to keep Minnesota ramp meters effective under scenarios with increased autonomy, and will address safety considerations for ramp metering in an automated future.
Prof. Nikos Papanikolopoulos is developing an autonomous mobile road asphalt density profiling robot for MnDOT. While MnDOT pavement construction personnel have improved quality assurance through use of new non-intrusive pavement profile sensors, the data acquisition process can be manually intensive, and hazardous as well. This project plans to deliver to MnDOT two low-cost, modular, mobile robot platforms designed specifically for pavement density profile testing. Modularity will be ensured by integrating separate, distributed, “plug-and-play” modules that can be re-utilized for other mobile platforms if the work leads to additional future implementations The mobile platforms are being assembled from widely available, low-cost, off-the-shelf components in order to minimize overall cost, recognizing that the landscape for such platforms is evolving rapidly.
Brian Davis (PI), Max Donath, and Nichole Morris are working on a project to further evaluate and improve the snowplow driver assistance system they have developed to provide real-time visual and audio feedback to operators working on snow removal and application of deicing agents. The system uses high-accuracy digital maps and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receiver to provide lane guidance, along with radar to detect forward obstacles. This system has been deployed in all 8 MnDOT districts as well as in Dakota County. Operator feedback has been positive, showing that the system is successful in providing drivers with useful support. Key improvements now being made include enhancements to the forward obstacle detection system and to the telemetry and data collection system, and development of new methods for mapping roadways.
Other examples of current intelligent transportation systems projects at UMN include:
- “CPS: Medium: Smart Tracking Systems for Safe and Smooth Interactions Between Scooters and Road Vehicles,” National Science Foundation Cyber-Physical Systems Program, Rajesh Rajamani (PI), Nichole Morris and Ju Sun.
- “Towards Edge-assisted Intelligent Driving: Cooperative Learning for Low-Latency, Safety-Critical and Secure V2I Communications & AV Remote Control,” College of Science and Engineering InterS&Ections Program, University of Minnesota, Zhi-Li Zhang (PI), Rajesh Rajamani and Jeff Caldor.
- “User-Centered Smart Traffic Sign Development,” Co-funded by Minnesota Department of Transportation and Local Road Research Board, Nichole Morris (PI) and Rajesh Rajamani.