University of Michigan’s augmented reality method makes CAV testing cheaper and safer


Augmented reality technology can accelerate the testing of connected and automated vehicles (CAVs) by 1,000 to 100,000 times and reduce additional testing costs beyond the price of physical vehicles to almost zero, according to a new white paper published by the University of Michigan’s Mcity Test Facility.

Developing automated vehicles comes with added challenges compared to conventional, driver-piloted cars and trucks. Beyond testing for dependability and occupant safety, driverless vehicles must work to prevent and avoid crashes, which requires testing countless crash scenarios, including those that rarely occur with conventional vehicles.

A team working at the U-M Transportation Research Institute (UMTRI) has developed a new and unique testing methodology that uses augmented reality, which combines the real world with a virtual world to create a faster, more efficient and economical approach to testing CAVs.

Led by Professor Henry Liu (top), the team borrowed from video gaming and other virtual technologies to create an augmented reality environment where real vehicles inside the safety of U-M’s Mcity Test Facility can interact with, and react to, computer-generated vehicles in real time through connected vehicle communications.

Researchers are able to create testing scenarios and interactions between test vehicles and computer-generated vehicles from the Michigan Traffic Laboratory at UMTRI, which is also the control center for the Mcity facility.

According to the white paper, an observer of such a test might see a test vehicle approach a traffic light and stop several yards short of the intersection to avoid rear-ending a computer-generated ‘virtual’ car already stopped at the light. The computer-generated virtual traffic elements are broadcast to Mcity test vehicles using a patent-pending, secure, wireless technology to allow both real and virtual vehicles to communicate with each other and the test-course infrastructure. This patent-pending technology was developed by Liu and Yiheng Feng, an assistant research scientist at UMTRI.

UMTRI researchers now test fully automated vehicles using three methods: closed-course testing; computer generated simulations; and operating vehicles or components on public roads. However, the researchers acknowledge that testing these new technologies on public roads comes with legal risks, exposure to liabilities and concern for public safety.

“In order for the public to accept and a widely adopt driverless vehicles we must be able to prove they are safe and trustworthy. This requires rigorous and extensive testing that would otherwise take more than a decade to accomplish,” noted Liu.

“Augmented reality testing is not only more efficient, it is safer and will allow us to ensure driverless vehicles operate dependably with the ability to prevent and avoid crashes. Our new procedure shows great potential to speed up and reduce the cost of testing.

“It also has the added benefit of allowing us to build a virtual library of computer-generated traffic scenarios that can be practiced without risk of damage or human injuries.”

U-M professor and director of Mcity Huei Peng added, “Most strategies for testing automated vehicles today fall short of what is needed to ensure the safety necessary to make driverless technology viable.

“The augmented reality environment at Mcity brings us a step closer by offering comprehensive, limitless testing scenarios that can be accomplished in a shorter period of time. That means testing is faster, cheaper and safer.”

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About Author


Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).