It’s important that self-driving cars quickly detect other cars or pedestrians sharing the road. Researchers at Carnegie Mellon University have shown that they can significantly improve detection accuracy by helping the vehicle also recognize what it doesn’t see.
Empty space, that is.
The very fact that objects in your sight may obscure your view of things that lie further ahead is blindingly 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 3-D data from lidar to represent objects as a point cloud and then try to match those point clouds to a library of 3-D representations of objects. The problem, Hu said, is that the 3-D data from the vehicle’s lidar isn’t really 3-D—the sensor can’t see the occluded parts of an object, and current algorithms don’t reason about such occlusions.