Sensor Trigger Classification for Edge-Case Training Data
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Solution Overview
Problem
Existing machine learning systems face challenges in obtaining sufficient and diverse training data, particularly for complex tasks like autonomous driving, which limits their performance and generalizability.
Innovation Solution
A system and method that rapidly generates training data by leveraging vehicles equipped with sensors to capture and classify specific image features or objects, using trigger classifiers to identify relevant data and transmit it for use in improving machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods of collecting, curating, and annotating training data are used, then data quality can be maintained, but the process requires significant time and resources
Solution Approach 1:
The system enables self-service data collection by automatically generating synthetic training data through simulation environments. The simulation system autonomously creates labeled training datasets without requiring manual annotation, thereby maintaining data quality while eliminating the time-consuming manual curation process.
Solution Approach 2:
The system performs preliminary action by pre-generating synthetic training data through simulations before actual deployment. By creating realistic training scenarios in advance through virtual environments, the system prepares high-quality labeled data that can be directly used for model training, avoiding the need for time-consuming field data collection and annotation.
2Reliability
If more training data is collected to improve model performance on specific use cases, then model accuracy improves, but the complexity and cost of data collection increases
Solution Approach 1:
The system uses copying by creating synthetic replicas of real-world scenarios through simulation environments. Instead of collecting diverse real-world data for every possible use case, the system generates copies of training scenarios programmatically, maintaining model performance while avoiding the complexity of physical data collection infrastructure.
Solution Approach 2:
The simulation-based system provides universality by using a single data generation platform to create training data for multiple different use cases and scenarios. This multi-functional approach allows the system to generate diverse training datasets without requiring separate complex collection systems for each specific application domain.
3Measurement precision
If manual annotation of training data is performed to ensure data accuracy, then data precision is maintained, but the process becomes tedious and resource-intensive
Solution Approach 1:
The system implements self-service annotation by automatically generating labeled training data through simulation environments. The synthetic data generation process inherently includes accurate labels and annotations without requiring human annotators, thereby maintaining data precision while dramatically increasing data generation productivity.
Solution Approach 2:
The system replaces the mechanical process of manual annotation with an automated computational approach. By using simulation engines and algorithms to generate and label training data automatically, the system substitutes human annotation efforts with machine-based processes, maintaining accuracy while eliminating the tedious and resource-intensive nature of manual work.
Data Source
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AI summary
Systems and methods for obtaining training data are described. An example method includes receiving sensor and applying a neural network to the sensor data. A trigger classifier is applied to an intermediate result of the neural network to determine a classifier score for the sensor data. Based at least in part on the classifier score, a determination is made whether to transmit via a computer network at least a portion of the sensor data. Upon a positive determination, the sensor data is transmitted and used to generate training data.