Safe AI Lab, Carnegie Mellon University: Smarter Testing

Testing self-driving vehicles demands rigor, especially given its practical challenges of real-world testing. For example, when we drive on a physical test track, it’s important that each test is as effective as possible in order to make the best use of time and resources. To get the most from this testing, ATG is supporting the research of Dr. Ding Zhao and the Safe AI Lab at CMU.

Here, machine learning is used to extract the most important interactions from a large body of observed data. This process of clustering and understanding distinct observed maneuvers could enable greater focus on the key scenarios that test self-driving vehicles in the most relevant ways. Working with Uber ATG’s real-world observed behaviors will allow the Safe AI Lab and Uber to advance this research at an entirely new scale.

“My team is developing methods to efficiently deploy autonomous vehicles by synthesizing tests from public road usage that can be applied to proving grounds using machine learning,” says Dr. Zhao.
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