CMU method makes more data available for training self-driving cars

For safety’s sake, a self-driving car must accurately track the movement of pedestrians, bicycles and other vehicles around it. Training those tracking systems may now be more effective thanks to a new method developed at Carnegie Mellon University. Generally speaking, the more road and traffic data available for training tracking systems, the better the results, and the CMU researchers have found a way to unlock a mountain of data.