HD Map Data Segmentation for Autonomous Simulation Meshes
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Solution Overview
Problem
Current methods for generating simulated driving environments for autonomous vehicle testing are inefficient due to the processing of excessive data, which can slow down the generation of 3D environmental representations and require more storage space, while also including unnecessary information.
Innovation Solution
The method involves filtering map data based on how autonomous vehicle software uses inputs from sensors like LIDAR and cameras, separating data into distinct topics, and generating meshes and textures for each topic, allowing for a more efficient and accurate simulation environment by reducing unnecessary data and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If all HD mapping data is processed to generate simulated driving environments, then the completeness and accuracy of the simulation environment is improved, but the processing time and storage requirements increase significantly
Solution Approach 1:
The patent segments HD mapping data into multiple distinct data topics (e.g., road geometry, traffic signs, pedestrians, buildings) and processes each topic separately to generate corresponding meshes. This segmentation allows the system to focus computational resources on essential elements first, reducing overall processing time while maintaining environmental accuracy.
Solution Approach 2:
The patent extracts and processes only the most critical data topics needed for autonomous vehicle testing (such as road geometry and traffic elements) while excluding less relevant data. This extraction approach maintains the accuracy of essential simulation components while significantly reducing processing time and storage requirements.
2Manufacturing precision
If all HD mapping data is processed to generate simulated driving environments, then the completeness of the simulation environment is improved, but the storage space requirements increase
Solution Approach 1:
By segmenting HD mapping data into distinct topics and generating separate meshes for each, the system stores only the essential geometric and textural information needed for simulation. This segmented approach reduces redundant data storage while maintaining environmental completeness.
Solution Approach 2:
The patent extracts only the necessary data elements required for autonomous vehicle testing scenarios, excluding unnecessary information. This extraction reduces storage space requirements while preserving the completeness of critical simulation environment components.
3Adaptability or versatility
If multiple textures are defined and applied to different data topics, then the realism and testing capability of the simulation environment is improved, but the complexity of the generation process increases
Solution Approach 1:
The patent assigns different textures to different data topics (e.g., road surfaces, vegetation, buildings, traffic signs) based on their semantic categories. This segmentation approach systematically manages texture complexity by organizing it according to data topics, making the generation process more structured and manageable.
4Productivity
If data is filtered based on sensor usage patterns, then the processing efficiency is improved, but the risk of omitting important data increases
Solution Approach 1:
The patent extracts and processes data topics that are most relevant to autonomous vehicle sensor systems (LIDAR, cameras, radar) while filtering out less relevant data. This extraction approach improves processing efficiency by focusing on sensor-critical data while maintaining reliability through selective inclusion of important elements.
Solution Approach 2:
The patent applies different processing qualities to different data topics based on their importance to autonomous vehicle operation. Critical data topics (road geometry, traffic signs) receive higher processing priority and more detailed treatment, while less critical topics receive streamlined processing, optimizing both efficiency and reliability.
Data Source
AI summary
A method may include obtaining HD mapping data including multiple data topics with one or more distinct instances. The method may include creating multiple meshes from corresponding distinct instances. The method may include defining multiple textures for each data topic. The method may include selecting a first data topic and a first distinct instance of the first data topic. The method may include selecting a first texture for the first data topic and applying the first texture to a first mesh corresponding with the first distinct instance. The method may include selecting a second distinct instance of the first data topic. The method may include selecting a second texture and applying the second texture to a second mesh corresponding with the second distinct instance. The method may also include combining the first mesh and the first texture with the second mesh and the second texture to generate a combined mesh.