Recent developments in the area of Artificial Intelligence (AI), specifically the advancement of deep learning and increased computing power, have made autonomous driving feasible for the first time. This blog describes the transition from code-centric algorithms into data-centric algorithms that are necessary for deep learning. Though the results achieved by these new methods are significant, they require large amounts of training data and raise problems around the storing and transmission of data from the autonomous vehicles.
As long as cars have existed, drivers have needed help getting from place to place (even if some folks are sometimes too proud to admit it). From bulky street maps that never seemed to fold back in the way they folded out, to thick Thomas Guide books that left many a passenger frantically flipping pages and plotting coordinates, navigation has always been a crucial part of the automotive experience.