Road Surface Texture Mapping Using Lane-Proximity Wear Artifacts
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Solution Overview
Problem
Existing computer-generated simulated driving environments lack realistic representations of road surface wear and use, such as tire tracks and fluid stains, due to inefficient processing of map data, leading to time-consuming manual adjustments in 3D modeling.
Innovation Solution
A simulation platform processes road map data to infer the location of visual artifacts by calculating distances from lane features, generating texture maps that adjust texel appearances based on surface reference lines, and applying these adjustments to a 3D polygon topology mesh to create realistic road surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustments are used to create road surface textures in 3D modeling, then visual realism can be achieved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent uses aerial imagery and map data as templates to automatically generate road surface textures, copying real-world visual information directly into the simulation environment. This eliminates manual texture creation while preserving visual realism, as the textures are derived from actual photographs and geographic data rather than being hand-crafted.
Solution Approach 2:
The system automatically processes map data, aerial imagery, and geographic information to generate its own road surface textures without requiring manual intervention. The simulation platform self-services by extracting visual features from available data sources and applying them to create realistic road surfaces, thereby eliminating the time-consuming manual adjustment process.
2Manufacturing precision
If detailed visual artifacts are generated for road surfaces, then realism is enhanced, but processing complexity increases
Solution Approach 1:
The patent segments the road surface into distinct regions based on map data features such as lanes, curbs, and intersections. Each segment is assigned appropriate visual artifacts from aerial imagery, allowing detailed realism to be achieved through systematic division of the processing task rather than attempting to handle the entire road surface as a single complex operation.
Solution Approach 2:
The system performs preliminary processing of aerial imagery and map data to identify and extract visual features before applying them to the road surface. By pre-processing the source data to identify relevant textures, patterns, and artifacts, the system reduces the complexity of the final rendering operation while maintaining high detail levels in the generated visual artifacts.
Data Source
AI summary
In various examples, a simulation platform generates a simulated driving environment by processing road map data to infer the location of wear-related visual artifacts for portions of a roadway surface. Using map data, the simulation platform generates texture maps for aesthetic road renderings that are used to apply textures onto a 3D polygon topology mesh. The simulation platform generates visual artifacts representing use and wear of the roadway surface based on calculating one or more distances from roadway lane features derived from the map data. The simulation platform computes distances associated with reference line data derived from an image to render texture from one or more roadway lane features. The distances are used in determining how the appearance of the texels are adjusted to include the wear-related visual artifacts when rendered on a roadway of the simulated driving environment.