Location-Aware Image Training Data Capture for Vehicle Navigation
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Solution Overview
Problem
Existing methods for obtaining image training data for machine learning models used in vehicle navigation and control are inefficient, leading to high costs and redundant data collection, particularly in capturing details relevant for map data generation and autonomous driving.
Innovation Solution
A method involving a camera controller that dynamically adjusts image recording based on location relevance, switching between video and single image modes depending on the location's importance for map data generation or vehicle control, thereby optimizing data capture and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video recording is used continuously to capture all training data, then comprehensive feature capture is achieved, but data rate and storage costs increase significantly
Solution Approach 1:
The patent applies local quality by differentiating recording strategies based on location characteristics. Urban areas with high relevance trigger video recording mode to ensure comprehensive feature capture, while rural areas with low relevance use single image mode to reduce data volume. This localized adaptation of recording quality resolves the contradiction between comprehensive capture and storage efficiency.
Solution Approach 2:
The system dynamically switches between video recording mode and single image mode based on real-time location relevance assessment. The camera controller adjusts recording behavior dynamically according to whether the vehicle is in an urban or rural area, enabling the system to optimize between comprehensive capture and data reduction adaptively throughout the journey.
2Quantity of substance
If single images are recorded continuously to reduce data volume, then storage costs are reduced, but important features may be missed
Solution Approach 1:
The patent applies local quality by differentiating recording strategies based on location characteristics. Urban areas with high relevance trigger video recording mode to ensure comprehensive feature capture, while rural areas with low relevance use single image mode to reduce data volume. This localized adaptation of recording quality resolves the contradiction between comprehensive capture and storage efficiency.
Solution Approach 2:
The system uses feedback from location relevance assessment to control recording mode. The camera controller continuously evaluates whether the current location is urban or rural and adjusts recording behavior accordingly, ensuring that video mode is activated when features are likely to be important while single image mode suffices in less critical areas.
3Productivity
If video recording is used in all areas, then all training data is captured uniformly, but redundancy increases particularly in rural areas
Solution Approach 1:
The patent applies local quality by differentiating recording strategies based on location characteristics. Urban areas with high relevance trigger video recording mode to ensure comprehensive feature capture, while rural areas with low relevance use single image mode to reduce data volume. This localized adaptation of recording quality resolves the contradiction between comprehensive capture and storage efficiency.
Solution Approach 2:
The system applies partial action by recording only what is necessary for each location type. Instead of uniformly applying video recording everywhere, it uses single image mode in rural areas where less data is needed, and reserves video mode for urban areas where comprehensive capture is more valuable, thus avoiding excessive data collection in low-relevance zones.
4Quantity of substance
If location-based relevance determination is added to optimize recording, then data efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies universality by using a multi-functional system that combines GPS location tracking, area type determination (urban/rural classification), and camera control in a single integrated setup. The camera controller serves multiple functions: it manages both video and single image recording modes, switches between them based on location, and coordinates with the location determination module, thereby reducing overall system complexity through functional integration.
Data Source
AI summary
Aspects concern a method for gathering image training data for training a machine learning model to detect features for vehicle navigation or vehicle control, comprising mounting a camera onto a vehicle or the driver of a vehicle, determining a location of the vehicle, determining a relevancy of the determined location for map data generation or vehicle control and recording a video with the camera if the determined relevancy is above a predetermined threshold and recording single images if the determined relevancy is below the predetermined threshold.