Mcity 2.0 is the next-generation autonomous vehicle test track. While capabilities are not yet fully operational, Mcity is accepting funding requests. Learn more about Mcity 2.0 and submit your request below.

Click or drag a file to this area to upload.
PDF format only. File size not to exceed 10 MB.

NSF National Science Foundation logo
About Mcity 2.0

The Mcity Test Facility opened in 2015 as the world’s first purpose-built proving ground for testing the performance and safety of connected and automated vehicles (CAVs) and technologies under controlled and realistic conditions.

Today, the facility is evolving into the next-generation autonomous vehicle test track thanks to a $5.1 million grant from the National Science Foundation. With NSF’s support, we’re 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.

Mcity 2.0 will give researchers easy, remote access to CAV testing resources, and will help create a more equitable playing field in mobility.

Funding to use Mcity 2.0 will be available to researchers already working on NSF projects who seek additional support.


Mcity 2.0 diagram

Mobility Data Center

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

The Mobility Data Center is a cloud-based data platform that is designed to ingest, archive, process, and share the large amount of data generated by the cooperative infrastructure located within Mcity 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 (is this Mcity OS?)
  • Data is collected from roadside sensors (cameras) that capture images (vehicles and non-motorised road users, such as pedestrians and cyclists as well as motor-cyclists 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
  • Potential collision warnings are generated and sent to the roadside unit to be communicated to relevant vehicles
  • Near-miss and crash events are archived in the Mobility Data Center
  • Data can be used to train automated vehicles and has Smart City applications
  • Richer data and easier to collect than on-road driving data

Learn more about the Mcity Mobility Data Center

Naturalistic Driving Environment

It is estimated that it would take a fleet of 100 automated vehicles, driving at 25 miles per hour for 5 billion miles to avoid 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, 8 commercial, simulation vendors have built digital twins of the physical Mcity test facility, which 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.

Source: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? 

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 vehicle and facility features and infrastructure. Mcity OS is built on our OCTANE API and SKYLINE app.

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.
To inquire about licensing, contact the University of Michigan’s Office of Technology Transfer.

Learn more about the Mcity Operating System (OS)

Safety Assessment

The Mcity Safety Assessment combines two key pieces of technology to the Mcity 2.0 toolkit: the Mcity ABC Test and the CCAT SAFE TEST.

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.

Accelerated evaluation: In this process, the first step is to collect naturalistic driving data that reflects what the test vehicles will face on public roads in normal conditions. The behavior of the human drivers is then “skewed” (based on importance sampling) to boost aggressive/risky behaviors and focus on difficult miles. This process is possible because the U-M and, in particular, the U-M Transportation Research Institute, has 20 years of experience leading field operational tests and collected tens of millions of miles of naturalistic driving data.

Behavior competence: In behavior competence testing, vehicles are put through a set of comprehensive scenarios to demonstrate their safety performance. In collaboration with researchers from the University of Michigan and the Mcity Leadership Circle, 50 scenarios have been compiled.

Corner Cases: There is no official definition of corner cases, other than the fact most experts agree these cases should be deterministic and should be at the very edges of the operating domain design. A corner case, for example, would involve an automated vehicle traveling at its highest speed with the most obstructed view, and facing a pedestrian running at the highest speed, and so on. Corner cases also can be designed to explore the known weakness of the sub-systems of the vehicle, such as asking a vehicle relying on cameras to recognize a black car on a dark night.


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). It is estimated that one mile on a test track using the SAFE TEST equates to 5,000 real-world miles.

Augmented Reality Test Environment: To add real vehicles to a test scenario, companies would have to spend thousands of dollars and hours to coordinate and control. The augmented reality environment allows researchers to add virtual, background traffic to Mcity that the test vehicle views as “real”. The simulated vehicles are easily controlled so specific test scenarios can be repeated perfectly each time.

Naturalistic and Adversarial Driving Environment: The NADE inserts background vehicles that conduct adversarial and rare maneuvers at a much higher rate, such as hard braking. Simultaneously, the environment uses naturalistic driving data from the University of Michigan Transportation Research Institute (UMTRI) to ensure unbiasedness.

Learn more about the Mcity Safety Assessment