Object Keypoint Label Consensus for Accurate Sensor Ground Truth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicle systems face challenges in accurately determining object keypoint locations from sensor readings due to errors in human-assigned labels in training data, which are time-consuming and costly to produce, leading to inefficient machine learning model training.
Innovation Solution
A system that automatically generates high-quality training data by determining estimated ground truth object keypoint labels through quality control of label data from multiple human providers, using techniques to compute consensus scores and improve labeling quality, enabling scalable and accurate 3D point cloud data processing for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-assigned labels are used for training data, then object keypoint locations can be determined, but label errors and high production costs occur
Solution Approach 1:
The patent combines multiple human provider labels for the same object keypoint into a single consolidated label. By merging multiple independent labeling efforts, the system achieves higher reliability and accuracy than any single provider could achieve alone, while reducing the impact of individual labeling errors.
Solution Approach 2:
The system implements a feedback mechanism where quality control scores are computed for each label based on agreement among multiple providers. Labels with low quality scores are identified and corrected through iterative refinement, allowing the system to learn from and improve upon initial labeling errors.
2Measurement precision
If multiple human providers are used for label data, then labeling quality improves, but time and cost increase
Solution Approach 1:
The system uses a threshold-based approach where labeling continues until a predetermined quality threshold is met. Rather than requiring exhaustive labeling by all possible providers, the process stops once sufficient agreement is reached, balancing quality improvement with time efficiency.
Solution Approach 2:
The patent replaces manual sequential labeling with an automated consensus-computing system. Quality control scores are automatically calculated from multiple provider inputs, and corrections are systematically applied, replacing time-consuming manual review processes with efficient computational methods.
3Measurement precision
If manual quality control is performed on label data, then label accuracy improves, but processing time increases
Solution Approach 1:
The system replaces manual quality control review with automated computation of quality control scores. By using algorithmic processing to evaluate label consistency across multiple providers, the system maintains high accuracy standards while dramatically increasing processing throughput compared to manual inspection.
Solution Approach 2:
The patent transforms quality control from a qualitative manual assessment to a quantitative automated scoring system. By defining specific parameters for label quality (consensus scores, agreement metrics), the system enables efficient computational evaluation that maintains rigor while improving productivity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining estimated ground truth object keypoint labels for sensor readings of objects. In one aspect, a method comprises obtaining a plurality of sets of label data for a sensor reading of an object; obtaining respective quality control data corresponding to each of the plurality of sets of label data, the respective quality control data comprising: data indicating whether the labeled location of the first object keypoint in the corresponding set of label data is accurate; and determining an estimated ground truth location for the first object keypoint in the sensor data keypoint from (i) the labeled locations that were indicated as accurate by the corresponding quality control data and (ii) not from the labeled locations that were indicated as not accurate by the corresponding quality control data.