When a human drives a car, they know that what they see at any given moment may not completely represent potential obstacles in their path. For example, a truck could block a human driver’s vision of a pedestrian crossing the street, but a reasonable driver would consider that someone could be in the crosswalk before proceeding when a traffic light turns green.
Autonomous vehicles struggle with that concept.
Technology developed at Carnegie Mellon University could help fill in these gaps with more data. By borrowing techniques from map-making, an automous vehicle can better interpret what its sensors are seeing, according to Deva Ramanan, associate professor of robotics at CMU. Ramanan is also the director of the CMU Argo AI Center for Autonomous Vehicle Research.