Self-driving cars are both fascinating and fear-inducing, as they must accurately assess and navigate the rapidly changing environment. Computer vision, which uses computation to extract information from imagery, is an important aspect of autonomous driving, with tasks ranging from low level, such as determining how far away a given location is from the vehicle, to higher level, such as determining if there is a pedestrian in the road.
Nathan Jacobs, professor of computer science & engineering in the McKelvey School of Engineering at Washington University in St. Louis, and a team of graduate students have developed a joint learning framework to optimize two low-level tasks: stereo matching and optical flow.
