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

Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry