Mcity Mobility Transportation Center

MCity Mobility Transportation Center

Increased awareness of Mcity and MTC after Mcity opened in 2015 helped spike interest from potential industry collaborators. Today, MTC’s Leadership Circle of industry partners has grown to 17 companies, and its roster of affiliate industry members numbers more than 40. Enthusiasm for MTC’s work is growing among researchers as well, with 29 R&D projects now underway.
The transformation to connected and automated mobility will be a game-changer for safety, efficiency, and energy, making life better in our cities and suburbs. MTC and Mcity signify a huge commitment on the part of U-M to advance these beneficial technologies.

The University of Michigan’s Mobility Transformation Center (MTC) is a public/private R&D partnership developing the foundations for a commercially viable ecosystem of connected and automated vehicles. MTC partners are drawn from industry, government, and academia to accelerate progress and shape the future of mobility. MTC’s central goal is to develop and implement an advanced system of connected and automated vehicles in Ann Arbor by 2021.

 

Mcity, a test lab at MTC, is designed to simulate urban and suburban roadways and provide a controlled environment for safe, repeatable testing of connected and automated vehicle technologies before they are tried out on public roads. In addition, MTC is developing three on-roadway deployments of thousands of vehicles in Ann Arbor and across the region, which will test ideas in real-world driving conditions.
As MTC Director Huei Peng notes, “Mcity will accelerate progress. It would take thousands of cars driving millions of miles on real streets for cars to encounter the challenges that can be readily simulated safely in Mcity, and repeated at will, to test connected and driverless vehicles.“

  • “It would take thousands of cars driving millions of miles on real streets for cars to encounter the challenges that can be readily simulated safely in Mcity”
    Huei Peng, Director, U-M Mobility Transformation Center

Mcity is unique–it’s not a test track, but a test environment for automated and connected vehicles of the future. Locating Mcity on U-M’s North Campus creates a space for many disciplines across U-M to come together to go beyond technical developments and address the legal, societal, regulatory, political, economic, business, consumer acceptance, and urban planning issues key to implementation.

Connected, automated, autonomous

  • “Connected” vehicles can anonymously and securely exchange data–including location, speed, and direction–with one another, and with the surrounding infrastructure, via wireless communication devices. This makes it possible to warn drivers of emerging dangerous situations, and to continuously adapt traffic signals to real-time traffic, easing congestion. Bicycles and pedestrians can also be connected via portable devices.

 

  • “Automated” vehicles allow certain driving functions–acceleration, braking, steering–to be machine-activated by technology built into the vehicle. Automation requires a variety of sensors, connections and maps to create situation awareness, and robotic functionality to mimic the role of a human driver. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity can serve as additional “sensors” that provide valuable information about other vehicles and features of the infrastructure. In this way, connectivity enables automated responses to warnings, and ultimately enables a vehicle to drive by itself.

 

  • “Autonomous” is something of a misnomer when applied to vehicles–the vehicle still needs to connect to other entities to update maps, GPS, and remote control commands. A fully autonomous vehicle drives itself without input or command from the outside, and does not rely on communications from other vehicles. Instead it carries sensors, decision-making software, and control features to “see” its environment and respond, just as a human driver would. Unlike connected vehicles, however, autonomous vehicles cannot detect traffic situations that are blocked by physical barriers, or out of range of their sensors.

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