Road Mesh and Tertiary Data for Scalable AV World Generation
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Solution Overview
Problem
Existing techniques for generating simulated environments are computationally intensive and time-consuming, limiting the ability to create scalable and accurate simulated worlds for training and validating autonomous vehicle systems, which is essential for safe navigation and decision-making.
Innovation Solution
The approach involves procedurally generating simulated environments using sensor data from real environments, integrating road network data with a road mesh, and supplementing it with tertiary data to create accurate and efficient simulated worlds, allowing for faster generation and reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual generation techniques are used to create simulated environments, then accuracy and detail can be achieved, but computational resources and time consumption increase significantly
Solution Approach 1:
The simulated environment is divided into multiple levels of detail: primary geometry from sensor data, secondary elements from tertiary data, and tertiary details procedurally generated. This segmentation allows different parts of the environment to be generated at appropriate detail levels, balancing accuracy with computational efficiency.
Solution Approach 2:
The system creates accurate copies of real-world environments by processing sensor data (LIDAR, camera, radar) to generate simulated environments that replicate real-world geometry, textures, and objects. This copying approach maintains high accuracy while enabling automated generation across multiple environments.
2Manufacturing precision
If manual generation techniques are used to create simulated environments, then detailed and accurate environments can be produced, but the process becomes time consuming
Solution Approach 1:
Sensor data from real environments is collected and processed in advance to create pre-rendered geometry, textures, and object models. This preliminary action stores detailed environmental data that can be quickly loaded and reused for multiple simulated scenarios, reducing generation time while maintaining detail quality.
Solution Approach 2:
Detailed environmental data is copied from real-world sensor measurements into the simulated environment, preserving high detail quality. Objects, surfaces, and geometries are replicated with accuracy while automated processing reduces the time required compared to manual creation.
3Reliability
If existing techniques are used to generate simulated environments, then comprehensive environments can be created, but computational resources are excessively consumed
Solution Approach 1:
Different regions of the simulated environment are rendered at different levels of detail based on their importance and visibility. Areas requiring high fidelity (e.g., road surfaces, traffic signs) are generated with maximum detail from sensor data, while peripheral areas use lower-detail procedural generation, reducing overall computational resource consumption while maintaining environmental completeness.
Solution Approach 2:
The system processes multiple sensor data types (LIDAR, camera, radar, GPS) through a unified pipeline that generates comprehensive simulated environments. This multi-functional approach consolidates computational tasks, improving efficiency while maintaining the completeness and reliability of the generated environments.
Data Source
AI summary
Procedural world generation using tertiary data is described. In an example, a computing device can receive road network data associated with the real environment and a road mesh associated with a real environment. The computing device can associate the road network data with the road mesh to generate a simulated environment. Additionally, the computing device can associate supplemental data with the road network data and the road mesh to enhance the simulated environment (e.g., supplementing information otherwise unavailable to the sensor data due to an occlusion). The computing device can output the simulated environment for at least one of testing, validating, or training an algorithm used by an autonomous robotic computing device for at least one of navigating, planning, or decision making.