Time-Series Ground Truth Generation for Autonomous Driving ML
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Solution Overview
Problem
The process of creating training data for deep learning systems, particularly for autonomous driving, is labor-intensive and inefficient due to the need for manual annotation and accurate labeling of features, which hinders the development of high-performance machine learning models.
Innovation Solution
A method that utilizes sensor data from vehicles to generate training datasets by capturing time series elements, including image and odometry data, to create accurate three-dimensional representations of features like lane lines, which are then used to train machine learning models for improved autonomous driving capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and labeling methods are used to create training data, then accuracy of labels can be maintained, but significant time and labor resources are required
Solution Approach 1:
The system uses automatically generated ground truth data from sensor fusion and tracking algorithms to label training data, eliminating the need for manual annotation. The autonomous vehicle's own sensors and processing systems generate the labels, making the system self-sufficient in creating training data without external human intervention.
Solution Approach 2:
The patent replaces the manual mechanical process of human annotators labeling data with an automated computational system that uses sensor fusion, object detection algorithms, and tracking to generate ground truth labels automatically, substituting human labor with machine-based automation.
2Reliability
If more training data is collected to improve model performance, then model accuracy improves, but the complexity and resources required for data collection and annotation increase
Solution Approach 1:
The system uses a multi-functional data processing pipeline that simultaneously performs sensor fusion, ground truth generation, and training data creation. The same sensor suite and processing algorithms serve multiple purposes: navigating the vehicle, generating labels, and creating training datasets, eliminating the need for separate specialized systems.
Solution Approach 2:
The autonomous vehicle generates its own training data using its operational sensors and processing systems, eliminating the need for separate data collection infrastructure. The vehicle's normal operation automatically produces labeled training data, making the system self-sufficient and reducing external complexity.
3Reliability
If difficult-to-label data is collected to address model weaknesses, then model improvement targets are addressed, but the difficulty and cost of accurate labeling increases
Solution Approach 1:
The patent replaces manual labeling efforts with automated ground truth generation using sensor fusion and tracking algorithms. This substitution makes it feasible to accurately label difficult data scenarios that would be prohibitively expensive or time-consuming for human annotators, such as occluded objects, fast-moving targets, or edge case scenarios.
Data Source
AI summary
Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.