Sensor Data Tagging With Multi-Model Filtering for AV Annotation
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently identifying and annotating relevant subsets of sensor data for training neural networks, leading to inefficient and time-consuming training processes.
Innovation Solution
A signal processing system uses multiple machine learning models to filter and mine sensor data based on metadata, identifying specific categories for targeted annotation and training, thereby improving the efficiency and accuracy of neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is used for annotation and training, then the neural network can learn from comprehensive data, but the annotation process becomes extremely time-consuming and costly
Solution Approach 1:
The patent segments the sensor data into different categories using multiple machine learning models (e.g., capture session collections, object types, environmental conditions). This segmentation allows the system to selectively annotate only relevant subsets of data rather than processing all sensor data, thereby reducing annotation time while maintaining training quality.
Solution Approach 2:
The patent applies preliminary filtering and categorization actions before the annotation process. Machine learning models pre-process sensor data to identify and tag relevant capture sessions, objects, and conditions. This preliminary action enables annotators to focus only on pre-identified relevant data, significantly reducing the time required for manual annotation.
2Productivity
If multiple machine learning models are used to filter sensor data, then annotation efficiency improves, but system complexity increases
Solution Approach 1:
The patent employs multiple machine learning models that serve universal functions across different data filtering tasks. Each model can identify various aspects of sensor data (capture sessions, objects, conditions) and their outputs can be combined and used together. This multi-functionality approach improves annotation efficiency by enabling comprehensive data filtering while managing system complexity through standardized model interfaces and shared processing infrastructure.
3Loss of time
If sensor data is filtered to identify specific subsets for annotation, then annotation costs are reduced, but the risk of missing relevant data increases
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously learn from annotation results and performance metrics. The models use feedback from identified capture session collections and annotation outcomes to refine their filtering accuracy. This feedback loop ensures that as the system processes more data, it becomes better at identifying relevant subsets, thereby reducing annotation costs without compromising data selection accuracy.
Data Source
AI summary
Provided are methods for customized tags for annotating sensor data, which can include receiving sensor data captured during a plurality of sensor data capture sessions, processing the sensor data using a plurality of machine learning models to identify a plurality of capture session collections represented in the sensor data, filtering the sensor data based at least partly on a user-specified category of the plurality of categories of capture session to identify a capture session collection, of the plurality of capture session collections, representing sensor data of one or more sensor data capture sessions that conforms to the user-specified category, and transmitting the sensor data of one or more sensor data capture sessions that conforms to the user-specified category to an end user computing device. Systems and computer program products are also provided.