Active Learning Image Ranking for Autonomous Vehicle Training Data
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Solution Overview
Problem
Autonomous vehicles face challenges in processing vast volumes of data from cameras, GPS, and sensors, which can limit navigation accuracy and efficiency, especially in updating and storing mapping data.
Innovation Solution
A system utilizing multiple cameras and an active learning system to analyze images, determining a relative priority ranking based on complexity and diversity levels, and selecting a subset of images for training a primary image analysis model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mapping technology is used to navigate, then navigation capability is provided, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the most critical and informative data points from the vast amount of sensor information. Instead of storing complete maps and all sensor data, the system identifies and stores only essential features, objects, and spatial relationships needed for navigation, significantly reducing data volume while maintaining navigation capability
Solution Approach 2:
The patent applies different data processing and storage strategies to different spatial regions and data types. Important objects and features are stored with high detail, while less critical areas use lower detail representations, optimizing the balance between data volume and navigation effectiveness
2Measurement precision
If vast volumes of data are collected and analyzed, then navigation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and filtering sensor data in real-time to identify and prioritize only the most informative data points before they are stored or analyzed further. This preliminary filtering reduces the volume of data that requires intensive processing, maintaining accuracy while reducing processing time
Solution Approach 2:
The patent dynamically adjusts processing parameters and thresholds based on the current driving context and data characteristics. The system changes analysis depth, resolution levels, and processing priorities adaptively, ensuring sufficient accuracy for current conditions while minimizing processing time through parameter optimization
3Measurement precision
If all captured images are processed for training, then model accuracy improves, but data storage needs and processing complexity increase
Solution Approach 1:
The patent extracts and selects only the most informative and representative images for training the deep learning model. Instead of processing all captured images, the system identifies and stores a curated subset that provides sufficient training data for high accuracy while significantly reducing storage requirements and processing complexity
Solution Approach 2:
The system applies different processing and storage quality levels to different images based on their informational value. High-quality detailed processing is applied to images containing critical navigation information, while lower-quality processing is applied to less informative images, optimizing the balance between model accuracy and processing complexity
Data Source
AI summary
Systems and methods analyze a data set including a plurality of images. In one implementation, at least one processor receives a plurality of images acquired by one or more cameras associated with at least one vehicle; and analyzes the plurality of images using an active learning system configured to determine a relative priority ranking among the plurality of images. The relative priority ranking indicates an ordered sequence for the plurality of images, and is determined based on at least one indicator, determined for each of the plurality of images, of a complexity level and a diversity level associated with representations of one or more objects represented in the plurality of images. The at least one processor then outputs information indicating the relative priority ranking among the plurality of images.