Mcity 2.0 - Mcity

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The Mcity Test Facility is evolving into the next-generation autonomous vehicle test track thanks to a $5.1 million grant from the National Science Foundation. Mcity 2.0 will give researchers easy, remote access to CAV testing resources, and will help create a more equitable playing field in mobility. Learn more about Mcity 2.0 and submit your request or questions below.

About Mcity 2.0

With NSF’s support, Dr. Henry Liu, Mcity Managing Director, Greg McGuire, and the team are enhancing the Mcity Test Facility by developing digital infrastructure that will overlay the physical test facility and create a cloud-based, augmented-reality CAV testbed that will be available to academic researchers nationwide.

Funding to use Mcity 2.0 will be available to researchers already working on NSF projects who seek additional support. While Mcity 2.0’s remote capabilities are not yet fully operational, the physical test facility and research vehicles can be used by NSF-funded researchers now. 48 test days per year have been allocated for NSF projects.

Mobility Data Center

Real-time municipal traffic data feeds Mcity’s Mobility Data Center.

The Mobility Data Center is a cloud-based data platform designed to ingest, archive, process, and share the large amount of data generated by cooperative infrastructure located within the Mcity Test Facility and the City of Ann Arbor.

Hosted on Amazon Web Services (AWS)
Website user interface allows for real-time monitoring, near-miss/crash event querying, and raw data querying
Data is collected from roadside sensors (cameras) that capture images (vehicles and non-motorized road users, such as pedestrians and cyclists as well as motorcyclists and persons with disabilities or reduced mobility and orientation)
The images are then processed for tracking information, trajectory prediction, and timing of potential collisions using machine learning
Near-miss and crash events are archived in the Mobility Data Center

Currently, vehicle trajectory data generated using the sensors equipped at the State Street/Ellsworth roundabout in Ann Arbor are available. As part of the Smart Intersections Project at U-M Transportation Research Institute (UMTRI), data from 21 intersections in Ann Arbor will be made available in the future.

View available datasets

Learn more about the live traffic provided by the Mobility Data Center

Naturalistic Driving Environment

In a 2016 report, the Rand Corp. estimated that it would take a fleet of 100 automated vehicles driving at 25 mph for 5 billion miles to demonstrate their ability to avoid crashes and fatalities in the real world. Computer simulation is vital for researchers to put their systems to the test, but access is limited in the academic realm. To date, eight commercial simulation vendors have built digital twins of the physical Mcity Test Facility that will be unlocked as part of the Mcity 2.0 rollout. This environment is also adversarial, meaning researchers can test against safety-critical scenarios such as hard braking, cut-ins, and more.

Learn more about the Naturalistic Driving Environment (NDE)

Mcity OS

Mcity OS makes it possible for researchers to create and execute complex, sophisticated, and easily repeatable test scenarios of connected vehicles, automated vehicles, and connected and automated vehicles – potentially saving testing time and costs, and accelerating product development.

Mcity OS runs on any internet-enabled device to control all the features of the Mcity Test Facility. This cloud-based open-source operating system gives users point-and-click control over interactions between vehicles and facility features and infrastructure.

Mcity OS tools can be integrated at other test facilities and in real-world environments as we lead the transformation to CAVs, CV2X, and smart cities. Mcity OS is also available for use at other test facilities. The American Center for Mobility is the first facility to license Mcity OS.

Mcity OS will be the tool researchers use to access Mcity 2.0.

Learn more about the complex text scenarios you can design with Mcity OS

Live Test

Mcity 2.0 will enable U.S.-based researchers to send their test algorithms and programs to Mcity, plug them into the test facility’s Mcity OS operating system, request specific conditions for testing and participate remotely as those parameters play out in Mcity’s combined real/virtual setting.

An example of the kind of AV testing that could be conducted using this approach is the Mcity Safety Assessment Program outlined below, which was developed by Mcity and the Center for Connected and Automated Transportation (CCAT), a regional transportation research center funded by the U.S. Department of Transportation. CCAT is based at U-M.

Mcity Safety Assessment Program

Scenario-based behavior competency test: Mcity ABC Test
Driving environment-based safety performance test: CCAT SAFE TEST

Both Mcity ABC Test and CCAT SAFE TEST are developed based on the large-scale naturalistic driving data collected by UMTRI and Mcity.

Mcity ABC Test

The Mcity ABC Test consists of three parts: Accelerated evaluation, Behavior competence, and Corner cases. Taken together, they are random, valid, fair, and comprehensive.

Read the Mcity ABC Test white paper here

CCAT SAFE TEST

The Safe AI Framework for Trustworthy Edge Scenario Tests, or SAFE TEST, combines two pieces of technology: the Augmented Reality Test Environment and the Naturalistic and Adversarial Driving Environment (NADE). The background vehicles and other traffic participants in the NADE are trained to execute adversarial maneuvers at selected moments to maximize testing efficiency while ensuring testing accuracy.

Read the CCAT NADE paper here

Learn more about remote live testing with Mcity 2.0

Research Resources

Mcity provides academic researchers using Mcity 2.0 several resources to support their work:

Available Datasets

Mcity is providing the following datasets as a part of the Mcity 2.0 project:

Dataset Name: Roundabout Trajectory Data
- Date Period: June 2022
- Details:Vehicle trajectory data perceived from raw video frames collected by the roadside perception system
- Download this dataset via GitHub
Dataset Name: Roundabout traffic conflict (ROCO)
- Date Period: First week of September 2022
- Details: Vehicle trajectories and labels of the conflict events
- Download this dataset via GitHub