Active Learning Data Selection for Autonomous Vehicle Training
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Solution Overview
Problem
Current active learning methods for autonomous vehicles do not consider environmental information, existing training data, or specific events when selecting data samples for machine learning model training, leading to inefficiencies and suboptimal performance.
Innovation Solution
The system intelligently selects useful data samples for training machine learning models by assigning importance scores based on trends in labeled data, environmental metadata, and confidence scores from object detection models, thereby focusing on underrepresented classes and uncertain predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If passive learning method accepts randomly selected training data, then data collection is simple, but training efficiency is low and expensive labeling is required
Solution Approach 1:
The system performs preliminary analysis of environmental metadata and object detection confidence scores before data selection. By pre-identifying underrepresented classes and uncertain predictions through trend analysis of labeled data, the system prepares criteria for intelligent data selection, eliminating the need for random selection and expensive manual labeling while improving training efficiency.
2Productivity
If active learning selects useful data for training, then training efficiency improves and data requirements are reduced, but current methods do not consider environmental information or existing training data
Solution Approach 1:
The system segments the data selection process into multiple independent analysis dimensions: environmental metadata analysis (location, time, conditions), object detection confidence scoring, and training data trend analysis. Each dimension is evaluated separately and combined through importance scoring, enabling the system to consider environmental context while maintaining training efficiency through structured active learning.
Solution Approach 2:
The patent adds new dimensions to data selection by incorporating environmental metadata (location, time, weather conditions) and object detection confidence scores as additional selection criteria. This multi-dimensional approach transforms traditional single-criterion active learning into a comprehensive selection framework that considers both data quality and environmental context, improving adaptability while maintaining training efficiency.
3Quantity of substance
If real-world datasets contain continuously collected data, then data completeness is high, but redundancy and irrelevant information increase
Solution Approach 1:
The system extracts and removes redundant and irrelevant data through intelligent selection criteria. By analyzing trends in labeled data and identifying underrepresented classes, the system extracts only the most useful data samples for training, eliminating redundancy while maintaining data completeness. The importance scoring function automatically filters out irrelevant information, achieving data purification without manual intervention.
Data Source
AI summary
Systems and methods for selecting data for training a machine learning model using active learning are disclosed. The methods include receiving a plurality of unlabeled sensor data logs corresponding to surroundings of an autonomous vehicle and identifying one or more trends associated with a training dataset comprising a plurality of labeled data logs. The methods also include selecting a subset of the plurality of unlabeled sensor data logs that have an importance score greater than a threshold, the importance score being determined based on the one or more trends. The subset of the plurality of unlabeled sensor data logs is used for updating the machine learning model to generate an updated model.