Nvidia's Method for Training Self-Driving Car AI in Simulations
Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing.
Original patent title: “Training, testing, and verifying autonomous machines using simulated environments”
Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing. Granted to Nvidia Corp in 2022 with 23 claims and 17 forward citations.
Key facts
Coverage
What does this patent actually cover?
This patent details a method for training artificial intelligence (AI) systems, particularly for autonomous vehicles. It involves creating a simulated world where a virtual object, like a car, exists. Virtual sensors on this object generate data that is then encoded to match the format of data from real-world sensors. This encoded data is fed into machine learning models, which are trained to produce outputs that control the virtual object's actions within the simulation. The system updates the simulation based on these AI outputs, creating a loop for continuous learning and verification. For example, a virtual camera in the simulation generates image data, which is then processed by AI trained on real camera data to decide how the virtual car should steer.
The gap
What does this patent NOT cover?
- Training AI using only real-world sensor data without any simulation.
- Testing AI models in the real world without prior simulation.
- Simulations where virtual sensor data format does not match real-world sensor data.
- Using AI models that are not based on machine learning.
- Updating the simulated environment without using AI model outputs.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The key innovation is the precise encoding of virtual sensor data to precisely match the format of real-world sensor data. This allows machine learning models trained on real data to be seamlessly tested and refined within a simulated environment, bridging the gap between virtual training and real-world performance.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Nvidia DRIVE Sim
Training AI for autonomous driving systems
Robotics simulation for robot training
Why it matters
The bigger picture
This patent is significant because it addresses a core challenge in developing autonomous vehicles: the immense cost and safety risks of training and testing AI solely in the real world. By enabling robust AI training within highly realistic simulations, it allows for faster iteration and validation of self-driving systems, paving the way for safer and more capable autonomous machines.
Filed
March 27, 2019
Granted
September 6, 2022
Market context
Who's building on this
Companies in this space
Nvidia, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a primary developer and user of this technology through its DRIVE Sim platform. Other major players in the autonomous vehicle space, including automakers and AI development companies, are likely using similar simulation techniques, potentially under licenselicensePermission from the patent owner to make, use, or sell the invention — usually in exchange for payment. Doesn't transfer ownership.Read more → or through their own research and development efforts.
Market impact
This patent contributes to the standardization of simulation-based training for autonomous systems. It has likely influenced the development of sophisticated simulation tools and platforms, enabling companies to accelerate the deployment of self-driving technology by reducing reliance on extensive real-world testing.
Claim 1 — Plain English
What this patent covers
This patent details a method for training artificial intelligence (AI) systems, particularly for autonomous vehicles. It involves creating a simulated world where a virtual object, like a car, exists. Virtual sensors on this object generate data that is then encoded to match the format of data from real-world sensors. This encoded data is fed into machine learning models, which are trained to produce outputs that control the virtual object's actions within the simulation. The system updates the simulation based on these AI outputs, creating a loop for continuous learning and verification. For example, a virtual camera in the simulation generates image data, which is then processed by AI trained on real camera data to decide how the virtual car should steer.
The clever bit
The key innovation is the precise encoding of virtual sensor data to precisely match the format of real-world sensor data. This allows machine learning models trained on real data to be seamlessly tested and refined within a simulated environment, bridging the gap between virtual training and real-world performance.
What it does not cover
- Training AI using only real-world sensor data without any simulation.
- Testing AI models in the real world without prior simulation.
- Simulations where virtual sensor data format does not match real-world sensor data.
- Using AI models that are not based on machine learning.
- Updating the simulated environment without using AI model outputs.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Strong
Citation count
25/40
Moderately cited
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$281K – $899K
Midpoint $562K · 12.8 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Taylor, Z., Heinrich, G., Campbell, M., Lebaredian, R., Cox, M., Tamasi, T., Delaunay, C., Daly, M., Beeson, C., Auld, D., Zedlewski, J., Hicok, G., & FARABET, C. (2022). Nvidia's Method for Training Self-Driving Car AI in Simulations (U.S. Patent No. 11,436,484). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11436484/alphazero
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Nvidia's Method for Training Self-Driving Car AI in Simulations cover?
Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing.
Who owns patent US 11436484?
Nvidia Corp owns this patent, granted in 2022.
When does this patent expire?
This patent is expected to expire on September 6, 2042, when the invention enters the public domain.
What is patent US 11436484 cited by?
This patent has been cited by 17 later patents that build on its ideas.
What problem does this patent solve?
This patent is significant because it addresses a core challenge in developing autonomous vehicles: the immense cost and safety risks of training and testing AI solely in the real world. By enabling robust AI training within highly realistic simulations, it allows for faster iteration and validation of self-driving systems, paving the way for safer and more capable autonomous machines.
What does this patent NOT cover?
Training AI using only real-world sensor data without any simulation.
Same assignee
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