Vehicle Data Auto-Labeling for Scalable ML Model Training
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Solution Overview
Problem
The challenge of managing and tuning machine learning models for diverse vehicles with vast data sets is complex, requiring significant computational resources and coordination, especially when models need to be tailored for individual vehicles or groups, leading to inefficiencies and fragmented deployment strategies.
Innovation Solution
A vehicle-based AI/ML Ops Pipeline that enables local data collection, labeling, and model training, combined with cloud-based federated learning, allowing for personalized and global model improvements through a cohesive framework that supports standardization, self-service tools, and continuous monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is gathered from connected vehicles for machine learning model improvement, then model accuracy and adaptability are improved, but the computational burden on backend servers increases significantly
Solution Approach 1:
The patent implements vehicle-specific configuration files that define custom data gathering and labeling parameters for each vehicle or vehicle group. This allows each vehicle to have tailored data collection strategies based on its specific needs, environment, and usage patterns, rather than applying a uniform global approach. The local customization reduces unnecessary data transmission and processing at the backend server level.
Solution Approach 2:
The system segments the data processing workload by enabling vehicles to perform data gathering, labeling, and preliminary model training locally using onboard computing resources. Configuration files are distributed to individual vehicles, and each vehicle independently manages its own data pipeline, separating the computational burden from the centralized backend server.
2Measurement precision
If manual labeling of vast amounts of vehicle data is performed, then data quality and model training effectiveness are improved, but the time and resources required become virtually impossible to complete
Solution Approach 1:
The patent implements automatic data labeling systems that operate autonomously on each vehicle using configuration files. The system automatically gathers data from sensors, applies appropriate labels based on pre-defined criteria in the configuration files, and prepares training data without human intervention. This self-service approach eliminates the need for manual labeling while maintaining data quality through automated validation rules.
Solution Approach 2:
Configuration files are prepared in advance with pre-defined data gathering parameters, labeling criteria, and model training specifications. These configuration files are distributed to vehicles before data collection begins, enabling the vehicles to automatically perform data labeling according to pre-planned instructions, eliminating the need for real-time manual labeling decisions.
Data Source
AI summary
A system receives identification of data to be gathered, via a request configured based on a configuration file associated with a machine learning model stored by a vehicle. The system receives identification of how the data is to be labeled, defined by the configuration file and create one or more topics for publication of the data, onboard the vehicle, the publication including both the gathered data and any meta-data usable to label the data in accordance with the definitions in the configuration file. Also, the system subscribes to the topics to receive the published data and the metadata and appends labels to the data, using the metadata, to label the data in accordance with the definitions for labeling in the configuration file. The system saves the labeled data in vehicle memory as data associated with the model.