Performance of autonomous vehicle operation in varying conditions by using imagery generated with machine learning for simulations

Abstract

In some embodiments, a computer-implemented method for simulating an unmanned aerial vehicle (UAV) to improve control system performance is provided. A computing system obtains ground truth aerial imagery for a region that depicts the region during a first state. The computing system determines a route for a simulated UAV within the region. The computing system generates, based on the ground truth aerial imagery, predicted aerial imagery that depicts portions of the region associated with the route. The computing system generates simulated aerial imagery that depicts portions of the region associated with the route during a second state different from the first state by providing the predicted aerial imagery to a machine learning model. The computing system simulates travel of the simulated UAV along the route during the second state by providing the simulated aerial imagery as simulated input to at least one control system of the simulated UAV.

CPC Classifications