Now, a team of MIT scientists are investigating an approach that leverages GPS-like maps and visual data to enable autonomous cars to learn human steering patterns, and to apply the learned knowledge to complex planned routes in previously unseen environments. Their work — which will be presented at the International Conference on Robotics and Automation in Long Beach, California next month — builds on end-to-end navigation systems architected by Daniel Rus, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
Rus and colleagues’ prior models followed roads without destinations in mind, while the new model drives to predefined places. “With our system, you don’t need to train on every road beforehand,” said first paper author and MIT graduate student Alexander Amini. “You can download a new map for the car to navigate through roads it has never seen before.”