3D Road Surface Feature Mapping for Sloped Line Detection
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Solution Overview
Problem
Existing 3D line detection methods assume a flat road surface, failing to account for elevation profiles, leading to inaccurate feature representation and detection in scenarios with steep slopes.
Innovation Solution
A method that projects image features into a three-dimensional feature space using surface hypotheses, allowing for the modeling of uphill and downhill slopes, and employs a machine learning model to learn and represent the road surface's elevation profile implicitly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverse perspective mapping is used to project features onto a flat ground plane, then the feature space represents the camera perspective, but it cannot accurately represent road surfaces with elevation profiles such as steep uphill or downhill slopes
Solution Approach 1:
The patent transitions from 2D inverse perspective mapping to 3D feature space representation. Instead of projecting features onto a flat ground plane, the system creates a three-dimensional feature space that incorporates elevation information, allowing accurate representation of both flat and sloped road surfaces while maintaining feature detection capability
Solution Approach 2:
The patent changes the fundamental parameters of the feature space from 2D coordinates to 3D coordinates, adding the elevation dimension. This parameter change enables the system to accommodate varying road surface geometries including steep slopes, while the machine learning model learns to interpret features in this new 3D parameter space
2Device complexity
If the road surface is assumed to be flat, then the feature space can be simply represented on a ground plane, but detection accuracy deteriorates in scenarios with steep slopes
Solution Approach 1:
The system adds a third dimension to the feature space to represent elevation, transforming the simple 2D ground plane into a 3D space that can accommodate sloped surfaces. This dimensional enhancement maintains relatively simple data structures while dramatically improving detection accuracy for various road geometries
Solution Approach 2:
Instead of using a static flat ground plane assumption, the system dynamically adapts the feature space to represent the actual road surface geometry through machine learning. The model learns the appropriate 3D feature representation for different scenarios, making the system flexible and adaptive to varying road conditions
Data Source
AI summary
A method for providing a three-dimensional feature map of a road surface. A computer program, a device, and a storage medium are also described.