  Vision-based learning methods for self-driving cars have primarily used
supervised approaches that require a large number of labels for training.
However, those labels are usually difficult and expensive to obtain. In this
paper, we demonstrate how a model can be trained to control a vehicle's
trajectory using camera poses estimated through visual odometry methods in an
entirely self-supervised fashion. We propose a scalable framework that
leverages trajectory information from several different runs using a camera
setup placed at the front of a car. Experimental results on the CARLA simulator
demonstrate that our proposed approach performs at par with the model trained
with supervision.
