USPTO Grants Patent for Autonomous Vehicle Operation Simulation

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted a patent (US12585954B2) to Wing Aviation LLC for a method of simulating autonomous vehicle operation using machine learning-generated imagery. The patent covers a computer-implemented method for improving control system performance by generating predicted and simulated aerial imagery for UAVs.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12585954B2 to Wing Aviation LLC, covering a novel computer-implemented method for simulating autonomous vehicle operation, specifically for unmanned aerial vehicles (UAVs). The patented technology utilizes machine learning to generate predicted and simulated aerial imagery, which is then used as input to test and improve the performance of UAV control systems under varying conditions. The filing date for this application was November 8, 2021.

This patent grant represents a new intellectual property asset in the field of autonomous systems and AI-driven simulation. While not a regulation imposing direct compliance obligations on other entities, it signifies innovation in AI applications for vehicle control and testing. Companies operating in the autonomous vehicle or drone sectors, particularly those involved in simulation and control system development, should be aware of this patent as it may impact their freedom to operate or necessitate licensing agreements if their technologies infringe upon the granted claims.

Source document (simplified)

← USPTO Patent Grants

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

Grant US12585954B2 Kind: B2 Mar 24, 2026

Assignee

Wing Aviation LLC

Inventors

Xinzhi Fan, Zaven Muradyan

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.