Temporal Label Transfer for Multi-Vehicle AV Training Data
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Solution Overview
Problem
Existing autonomous vehicle (AV) training systems face inefficiencies in collecting and interpreting diverse data sets for enhanced decision-making, particularly due to the time-consuming and error-prone process of manual labeling of sensor data from multiple perspectives.
Innovation Solution
Utilizing labeled data from one vehicle to automatically label data from another vehicle, leveraging a removable hardware pod with sensors to collect and synchronize data, and applying time stamps for efficient data processing and model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of sensor data from multiple vehicles is performed, then data accuracy can be ensured, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent uses labeled data from a first vehicle as a template to automatically generate labels for corresponding objects in sensor data from a second vehicle. This copying approach transfers accurate labels without manual intervention, resolving the contradiction by maintaining labeling accuracy while eliminating time-consuming manual processes.
Solution Approach 2:
The system enables automatic self-labeling of sensor data by utilizing the labeled dataset from the first vehicle to automatically annotate the second vehicle's data. This self-service mechanism eliminates the need for manual labeling while preserving accuracy through the transfer learning approach.
2Adaptability or versatility
If diverse sensor data from multiple vehicles is collected, then training data diversity improves, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary processing system that standardizes and synchronizes data from multiple vehicles using temporal correlation and spatial transformation. This intermediary layer manages the complexity of processing diverse multi-vehicle data while preserving data diversity for improved training.
Solution Approach 2:
The system segments the complex task of multi-vehicle data processing into distinct steps: temporal correlation of timestamps, spatial transformation to common coordinate systems, and selective label transfer. This segmentation reduces processing complexity by breaking down the overall task into manageable components.
3Reliability
If all sensor data is manually labeled, then complete accuracy is achieved, but resource consumption increases
Solution Approach 1:
The patent applies partial labeling by transferring labels only for objects detected by both vehicles, rather than manually labeling all objects in all datasets. This partial action approach maintains reliability for critical objects while reducing overall computational resource consumption.
Data Source
AI summary
In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.