Active Learning Data Sample Selection for Autonomous Vehicles
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Solution Overview
Problem
The process of annotating data samples for machine learning models, particularly in autonomous driving applications, is tedious and time-consuming due to the need for human annotators to label thousands of images and video samples, which is inefficient and costly.
Innovation Solution
An active learning system that automatically selects data samples for annotation using bounding box prediction confidence, spatial probability density, and ensemble score variance from multiple machine learning models, while excluding samples violating digital map constraints and detecting temporal inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all data samples are annotated by human annotators, then the training data quality is improved, but the time and cost increase significantly
Solution Approach 1:
The system uses machine learning models to automatically evaluate and select data samples that need annotation, reducing reliance on manual review of all samples. The active learning algorithm self-manages the selection process based on uncertainty metrics and data quality assessments
Solution Approach 2:
The system dynamically adjusts selection criteria parameters such as uncertainty thresholds, data quality weights, and sampling rates to optimize the balance between annotation quality and time consumption based on available resources and performance goals
2Quantity of substance
If all data samples are annotated by human annotators, then the training data completeness is improved, but the cost increases significantly
Solution Approach 1:
The system automatically identifies and prioritizes which data samples require annotation based on their informational value and model uncertainty, eliminating the need for costly manual annotation of all samples
Solution Approach 2:
The system annotates only the critical subset of data samples that provide maximum learning value, rather than annotating all available samples, achieving sufficient training data quality with reduced cost
3Reliability
If a large number of data samples are selected for annotation, then the model training coverage is improved, but the processing efficiency decreases
Solution Approach 1:
The system dynamically adjusts the sampling rate and selection thresholds based on data characteristics, model performance metrics, and resource constraints to optimize the trade-off between training coverage and processing efficiency
Solution Approach 2:
The system selects a strategically optimized subset of data samples for annotation that provides sufficient training coverage without requiring processing of all available samples, improving efficiency while maintaining reliability
4Measurement precision
If manual annotation of all data samples is performed, then the data labeling accuracy is improved, but the automation level remains low
Solution Approach 1:
The system automatically evaluates data samples using machine learning models to determine which ones require human annotation, self-managing the selection process based on uncertainty and quality metrics
Solution Approach 2:
The system introduces an automated pre-screening layer that acts as an intermediary between raw data and human annotators, filtering and prioritizing samples based on model uncertainty and data quality assessments
Data Source
AI summary
Among other things, we describe techniques for automatically selecting data samples for annotation. The techniques use bounding box prediction based on a bounding box score distribution, spatial probability density determined from bounding box sizes and positions and an ensemble score variance determined from outputs of multiple machine learning models to select data samples for annotation. In an embodiment, temporal inconsistency cues are used to select data samples for annotation. In an embodiment, digital map constraints or other map-based data are used to exclude data samples from annotation. In an exemplary application, the annotated data samples are used to train a machine learning model that outputs perception data for an autonomous vehicle application.