A New Method to Generate Data for Training Autonomous Vehicles - News

It goes without saying that an autonomous vehicle (AV) must be able to track the movement of pedestrians, animals, bicycles accurately, and other vehicles around it to safely and effectively get from point A to B. The systems responsible for doing this depend on being fed data, among other things, from which it is "trained" and learns to spot and react to these obstacles and hazards. A technique developed by Carnegie Mellon University (CMU) researchers called "scene flow" may be able to deliver improved results by training systems on larger datasets. Generally speaking, the more data that is available for training tracking systems, the better the results will be. And, according to the CMU researchers, they have found a way to unlock a "mountain" of autonomous driving data for exactly that purpose. Most AVs navigate based on sensor data from light detection and radar (LiDAR) systems that scan the environment to generate three-dimensional information of the world surrounding the vehicle.