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

Qadeer Khan · Patrick Wenzel · Daniel Cremers

Keywords: [ Applications ] [ Computer Vision ]

[ Abstract ]
Wed 14 Apr 6 a.m. PDT — 8 a.m. PDT


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.

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