Image-to-World Coordinate Transformation for Lane-Line Ground Truth
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Solution Overview
Problem
Conventional methods for generating ground truth labels for autonomous vehicle training are time-consuming and inefficient, often requiring substantial human interaction and computationally expensive processing to convert 2D image coordinates to 3D world space, leading to reduced quality and quantity of labeled data.
Innovation Solution
A system that projects image space coordinates from vehicle-mounted sensors to 3D vehicle space and then transforms them to 3D world space using known coordinates and relative positioning, enabling efficient generation of ground truth data by correlating temporal vehicle trajectories with 3D world space coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional human labeling of 2D video frames is used to generate ground truth labels, then lane line positions can be marked, but the process is time-consuming and requires substantial human interaction
Solution Approach 1:
The patent uses simulated vehicle trajectories and sensor data to generate synthetic ground truth labels that copy real-world driving scenarios. Instead of manually labeling each frame, the system creates virtual representations of lane lines and vehicle positions that can be directly used for training neural networks, dramatically reducing labeling time while maintaining accuracy
Solution Approach 2:
The system enables self-labeling by using the vehicle's own sensor data (cameras, LIDAR, GPS) and simulated trajectories to automatically generate ground truth labels. The vehicle's motion data and sensor measurements serve as their own labeling reference, eliminating the need for external human annotators
2Measurement precision
If 3D world space positions are derived from 2D image coordinates using per-frame LIDAR fusion, then accurate ground truth can be obtained, but computationally expensive processing is required
Solution Approach 1:
The patent performs preliminary transformation by pre-computing the relationship between 2D image coordinates and 3D world space positions using simulated trajectories. Instead of performing expensive LIDAR fusion for each frame, the system pre-establishes coordinate transformation models that can be applied efficiently to generate ground truth labels without real-time computational overhead
Solution Approach 2:
The system extracts essential geometric relationships from simulated vehicle trajectories and sensor data to create simplified transformation models. By separating the coordinate transformation logic from expensive LIDAR processing, the system obtains 3D positions through efficient mathematical transformations rather than computationally intensive point cloud fusion
3Productivity
If frames are skipped to reduce labeling burden, then less human interaction is required, but the quantity and quality of labeled ground truth data is reduced
Solution Approach 1:
The system generates synthetic copies of ground truth data from simulated trajectories, creating comprehensive labeled datasets without skipping frames. By virtualizing the labeling process through simulation, the system can process all available frames and generate abundant training data without the limitations of manual annotation throughput
Data Source
AI summary
In various examples, image space coordinates of an image from a video may be labeled, projected to determine 3D vehicle space coordinates, then transformed to 3D world space coordinates using known 3D world space coordinates and relative positioning between the coordinate spaces. For example, 3D vehicle space coordinates may be temporally correlated with known 3D world space coordinates measured while capturing the video. The known 3D world space coordinates and known relative positioning between the coordinate spaces may be used to offset or otherwise define a transform for the 3D vehicle space coordinates to world space. Resultant 3D world space coordinates may be used for one or more labeled frames to generate ground truth data. For example, 3D world space coordinates for left and right lane lines from multiple frames may be used to define lane lines for any given frame.