20230136766. METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION simplified abstract (STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY)
Contents
- 1 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION
Organization Name
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Inventor(s)
Ryan Knuffman of Danvers IL (US)
METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230136766 titled 'METHODS AND SYSTEMS FOR USING TRAINED GENERATIVE ADVERSARIAL NETWORKS TO IMPUTE 3D DATA FOR VEHICLES AND TRANSPORATION
Simplified Explanation
The abstract describes a method and system for generating high-resolution maps using a trained generative adversarial network (GAN) and navigation data.
- The method involves receiving a navigation data set.
- A trained generative adversarial network is used to generate a combined data set.
- The combined data set is then used to generate a high-resolution map with spatial data.
Potential Applications
- Autonomous driving: The high-resolution maps can be used by autonomous vehicles for navigation and obstacle detection.
- Urban planning: The maps can provide detailed spatial data for urban planners to analyze and design cities.
- Emergency response: The high-resolution maps can aid emergency responders in navigating and locating resources during crises.
Problems Solved
- Limited resolution: The use of a trained GAN allows for the generation of high-resolution maps, overcoming the limitations of traditional mapping techniques.
- Data integration: By combining navigation data with the GAN-generated data set, the method provides a comprehensive and accurate representation of the environment.
Benefits
- Improved accuracy: The use of a trained GAN enhances the quality and accuracy of the generated maps.
- Cost-effective: The method eliminates the need for expensive and time-consuming manual mapping processes.
- Real-time mapping: The system can generate high-resolution maps in real-time, allowing for dynamic updates and adaptability.
Original Abstract Submitted
a method includes receiving a navigation data set; generating a combined data set using a trained generative adversarial network; and generating a high resolution map that includes spatial data. a computing system includes: one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed, cause the computing system to: receive a navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data. a non-transitory computer-readable medium includes computer-executable instructions that, when executed, cause a computer to: receive a navigation data set; generate a combined data set using a trained generative adversarial network; and generate a high resolution map that includes spatial data.
