17947796. FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE simplified abstract (GM Cruise Holdings LLC)
Contents
- 1 FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE
Organization Name
Inventor(s)
Zhao Chen of Mountain View CA (US)
Yuning Chai of San Mateo CA (US)
FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17947796 titled 'FIRST-ORDER UNADVERSARIAL DATA GENERATION ENGINE
Simplified Explanation
The disclosed technology involves generating synthetic driving scenes with reduced safety metrics for testing and training autonomous vehicle systems.
- Encoding model receives driving data of a first scene, generates feature vectors, processes them to create a second set of feature vectors, and ultimately generates a second driving scene.
- The technology can be used for testing and training various systems of an autonomous vehicle.
- Systems and machine-readable media are provided to implement the method.
Potential Applications
The technology can be applied in the development and testing of autonomous vehicle systems, simulation training for AV operators, and research in the field of artificial intelligence and computer vision.
Problems Solved
The technology addresses the need for realistic yet safe driving scenarios for testing and training autonomous vehicle systems, which can be challenging to create in real-world environments.
Benefits
- Improved safety in testing and training environments for autonomous vehicles - Cost-effective and scalable solution for generating diverse driving scenarios - Enhanced efficiency in developing and validating AV systems
Potential Commercial Applications
"Enhancing Autonomous Vehicle Testing and Training with Synthetic Driving Scenes" can be used in the automotive industry for AV development, simulation software companies, and research institutions focusing on autonomous driving technologies.
Possible Prior Art
One possible prior art could be the use of simulation software for testing autonomous vehicles, but the specific method of generating synthetic driving scenes with reduced safety metrics may be a novel aspect of this technology.
Unanswered Questions
How does this technology compare to traditional methods of testing autonomous vehicles?
This technology offers a more controlled and customizable approach to creating driving scenarios compared to traditional methods that rely on real-world data or physical testing. It allows for the generation of diverse and challenging scenarios in a safe virtual environment.
What are the potential limitations or challenges of implementing this technology in real-world applications?
One potential challenge could be ensuring that the synthetic driving scenes accurately reflect real-world driving conditions to effectively train and test autonomous vehicle systems. Additionally, there may be limitations in the scalability and complexity of scenarios that can be generated using this technology.
Original Abstract Submitted
The disclosed technology provides solutions for generating synthetic driving scenes and in particular, for generating driving scenes with reduced safety metrics for use in testing and/or training various systems of an autonomous vehicle (AV). A method of the disclosed technology can include steps for receiving, at an encoding model, driving data representative of a first driving scene, wherein the encoding model is configured to generate a first set of feature vectors based on the driving data, processing the first set of feature vectors to generate a second set of feature vectors, and processing the second set of feature vectors to generate a second driving scene. Systems and machine-readable media are also provided.
