Geospatial Region Clustering for Autonomous Training Data Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Curating large training data sets for autonomous driving systems that include diverse geographic regions is challenging due to physical accessibility issues, high costs, and regulatory constraints, making it impractical to collect sufficient data for machine learning models, especially in regions with limited market access or complex geographical conditions.
Innovation Solution
The use of geospatial clustering techniques with deep neural networks and map data to group similar geographic regions based on perceptual characteristics, allowing for the sharing of training data across clustered regions, thereby reducing the need for extensive data collection in each specific area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large training data sets are curated for diverse geographic regions, then model accuracy and precision improve, but data collection costs and complexity increase significantly
Solution Approach 1:
The patent merges geographically dispersed training data by clustering regions with similar visual characteristics into semantic regions. Data from multiple geographic locations are combined into unified training sets for each semantic region, allowing diverse data sources to be integrated while reducing overall collection complexity.
Solution Approach 2:
The patent creates synthetic copies of training data through rendering engines that generate realistic sensor data in virtual environments. These synthetic data copies supplement real-world data, enabling model training without requiring extensive physical data collection from challenging geographic regions.
2Adaptability or versatility
If data collection is conducted in regions with limited accessibility or regulatory constraints, then model coverage improves, but collection costs and time increase
Solution Approach 1:
The patent performs preliminary clustering of geographic regions into semantic regions before data collection. This pre-organization allows data collection efforts to be focused on representative locations within each semantic region, reducing travel time and regulatory hurdles while maintaining comprehensive geographic coverage.
Solution Approach 2:
The patent introduces synthetic data generation as an intermediary between physical data collection limitations and model training requirements. When physical access to certain regions is restricted, synthetic data serves as a mediator to provide the necessary training examples without requiring actual presence in constrained geographic areas.
3Reliability
If extensive data collection is performed for each specific geographic region, then region-specific model performance improves, but overall data curation costs increase
Solution Approach 1:
The patent creates universal training data sets for each semantic region that can be applied across multiple geographic locations sharing similar characteristics. A single training data set serves multiple geographic regions simultaneously, reducing redundant data collection while maintaining region-specific model performance through semantic rather than geographic segmentation.
Data Source
AI summary
In various examples, a cell model that partitions a geographic region into one or more cells is used to determine clusters of cell which share similarities. Sensor data is provided to one or more machine learning models trained to classify the sensor data to one or more cells of the cell model. Based on classifying sensor data to cells of a cell model, similarities between pairings of cells of the cell model may be determined and used to form clusters of the cell which are sufficiently similar in order to aid in the curation of training data used to train machine learning models in order to aid an autonomous or semi-autonomous machine in a surrounding environment.