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Researchers at Carnegie Mellon University (CMU) have demonstrated that they can significantly improve detection accuracy in self-driving cars by helping the vehicle recognize what it does not see.
Self-driving vehicles 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 with that, according to Peiyn Hu, a Ph.D. student in CMU’s Robotics Institute, is that the 3D data from the vehicle’s lidar isn’t exactly 3D — the sensor can’t see the occluded parts of an object, and current algorithms don’t reason about such occlusions.
New CMU research shows that what a self-driving car doesn’t…