Researchers at Carnegie Mellon University have shown that they can significantly improve the accuracy of autonomous vehicles’ detection of objects such as other cars or pedestrians by helping the vehicle also recognize what it doesn’t see.
That objects in your sight may obscure your view of things that lie further ahead is obvious to people. But Peiyun Hu, a Ph.D. student in CMU’s Robotics Institute, said that’s not how self-driving cars typically reason about objects around them.
Rather, they use 3D data from lidar to represent objects as a point cloud and then try to match those point clouds to a library of 3D representations of objects. The problem, Hu said, is that the 3D data from the vehicle’s lidar isn’t really 3D—the sensor can’t see the occluded parts of an object, and current algorithms don’t reason about such occlusions…
In addition to Hu and Ramanan, the research team included Jason Ziglar of Argo AI and David Held, assistant professor of robotics. The Argo AI Center supported this research.