18058998. OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE simplified abstract (GM Cruise Holdings LLC)
Contents
- 1 OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE - 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
OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE
Organization Name
Inventor(s)
Russell Schaaf of Carlsbad CA (US)
OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18058998 titled 'OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE
Simplified Explanation
The abstract of the patent application describes a method for optimizing the generation of road model data for testing and evaluating autonomous vehicles in a simulated road environment. By selecting a minimal number of map sections to be generated and determining their locations, the optimizer can achieve the desired coverage amount for the test suite while reducing computational resources.
- Optimizing road model data generation for testing autonomous vehicles in simulated road environments
- Selecting a minimal number of map sections to be generated and determining their locations
- Reducing computational resources needed for generating road model data
- Achieving desired coverage amount for the test suite
Potential Applications
The technology can be applied in the development and testing of autonomous vehicles, robotics, and virtual reality simulations.
Problems Solved
1. Reducing computational resources needed for generating road model data 2. Improving the accuracy of testing and evaluating vehicle controls and dynamics in a simulated environment
Benefits
1. Cost-effective testing and evaluation of autonomous vehicles 2. Enhanced accuracy in simulating road environments 3. Efficient use of resources in generating road model data
Potential Commercial Applications
Optimizing road model data generation for autonomous vehicle testing and development
Possible Prior Art
There may be existing methods or systems for generating road model data for simulations, but the specific optimization technique described in this patent application may be novel.
Unanswered Questions
How does this technology compare to existing methods for generating road model data in terms of accuracy and efficiency?
The article does not provide a direct comparison between this technology and existing methods for generating road model data.
What are the potential limitations or challenges in implementing this optimization technique for road model data generation?
The article does not address any potential limitations or challenges that may arise in implementing this optimization technique.
Original Abstract Submitted
Autonomous vehicles are tested and evaluated on the road and in simulated road environments. When testing and evaluating autonomous vehicles in a simulated road environment, having detailed three-dimensional geometries and characteristics of road surfaces in the form of road model data can greatly improve a simulated environment's ability to test and evaluate vehicle controls and dynamics, and can generate a more accurate pose for the autonomous vehicle in simulation. However, generating road model data is computationally expensive, and it is not desirable to generate road model data for an entire map to cover all test scenarios. An optimizer can reduce the amount of resources needed by selecting a least number of map sections to be generated and determining the locations of the map sections to be generated that achieves a desired coverage amount for the test suite.
