In March 2012 Alex Teichman was a Ph.D. student in Stanford University’s computer science department, working on self-driving cars. His goal: to help a self-driving car understand the environment around it, in particular, to be aware of pedestrians, bicyclists, and other moving objects that might come into its path. His approach: instead of traditional image analysis, use depth information about objects gathered by laser rangefinders or sensors to define them, and then teach the computer to learn about the objects by “following” them as they move about the scene.
While the math he developed to implement this is complex, Teichman says the basic idea is simple. “Have you ever seen a child ride up a glass elevator that looks down on the street? At ground level, a car parked out front looks normal and uninteresting, but as she rides upwards, things gradually start looking very different. She’s probably seeing the world in a very different way and perhaps giggling about it. And her visual systems are learning: ‘Hey, that’s what a car looks like form above, I’ve never seen that before, cool!’” ….[Read more]