Vehicle ML Model Library for Semantic Observation Updates
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Solution Overview
Problem
Current systems for vehicle map services lack an efficient method to provide and update machine-learned models for detecting environmental states using sensor data from vehicles, leading to outdated models and inefficient data distribution.
Innovation Solution
A computer-implemented system that hosts a library of machine-learned models trained to generate semantic observations, allowing for the selection, update, and sharing of models based on sensor configurations, with feedback and performance metrics, enabling continuous improvement and distribution across a distributed computing system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learned models are updated continuously to improve detection accuracy, then model quality improves, but data distribution complexity and computational resource requirements increase
Solution Approach 1:
The system segments the monolithic model update process into modular components: model training modules, validation modules, version control modules, and distribution modules. Each component handles specific aspects of the update pipeline, reducing overall system complexity while enabling continuous improvement of detection accuracy through targeted model refinements.
Solution Approach 2:
The patent introduces intermediary components including model registries that store and manage model versions, validation systems that assess model performance before deployment, and version control mechanisms that track changes. These intermediaries buffer the complexity between model training and distribution, allowing accurate models to be updated without directly complicating the data distribution infrastructure.
2Measurement precision
If sensor data from multiple vehicles is collected and processed centrally, then model training quality improves, but data transmission time and network bandwidth consumption increase
Solution Approach 1:
The system segments the centralized data collection process by dividing sensor data into categorized groups (e.g., environmental conditions, sensor types, geographic regions). This segmentation allows selective transmission of only relevant data subsets to central training systems, reducing transmission time while maintaining model training quality through targeted data selection.
Solution Approach 2:
The patent implements preliminary data preprocessing and filtering at edge devices before transmission. Sensor data is validated, cleaned, and pre-processed locally, with only high-quality, relevant datasets transmitted to central systems. This preliminary action reduces network bandwidth consumption and transmission time while ensuring the central system receives optimal training data.
3Adaptability or versatility
If machine-learned models are updated frequently to adapt to changing environments, then adaptability improves, but system stability and reliability may deteriorate due to frequent changes
Solution Approach 1:
The system implements feedback mechanisms where model performance is continuously monitored after deployment. Validation metrics and performance data flow back to the training system, enabling controlled updates only when improvements are verified. This feedback loop maintains system stability by preventing premature or unvalidated model changes while allowing adaptability through evidence-based updates.
Solution Approach 2:
The patent introduces cushioning mechanisms including model validation queues, performance threshold checks, and rollback capabilities. Before deploying updated models, they undergo rigorous validation to ensure stability. If updates cause performance degradation, the system automatically rolls back to previous stable versions, cushioning against reliability deterioration while maintaining adaptability through controlled evolution.
Data Source
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AI summary
Provided are methods, systems, devices, and tangible non-transitory computer readable media for providing data associated with a machine-learned model library. The disclosed technology can perform operations including providing a machine-learned model library that includes a plurality of machine-learned models trained to generate semantic observations based on sensor data associated with a vehicle. Each machine-learned model of the plurality of machine-learned models can be associated with one or more configurations supported by each machine-learned model. A request for a machine-learned model from the machine-learned model library can be received a remote computing device. Furthermore, based on the request, the machine-learned model can be provided to the remote computing device.