Time-Series Ground Truth Generation for 3D Lane Training Data
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Solution Overview
Problem
The existing deep learning systems for autonomous driving face challenges in generating accurate training data due to the manual and resource-intensive process of collecting, curating, and annotating data, particularly for improving model performance.
Innovation Solution
The proposed solution involves using sensor data from vehicles to create a training dataset for machine learning models. This includes capturing image data and vehicle operating parameters, such as odometry, to generate a time series of elements. By analyzing this time series, a ground truth is determined and associated with a subset of the elements, enabling the creation of training data that can predict three-dimensional representations of features like lane lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and annotation is used, then data quality can be ensured, but significant resources and time are required
Solution Approach 1:
The system performs preliminary actions by automatically collecting sensor data and generating training datasets before manual annotation is needed. Vehicle sensors continuously capture image data and operating parameters, which are pre-processed into structured training data with ground truth labels, reducing the time required when manual annotation becomes necessary.
Solution Approach 2:
The system enables self-service by automatically generating training data with ground truth labels without requiring manual intervention. The vehicle's own sensor data and operating parameters are used to create self-labeled training datasets, eliminating the need for external annotators and significantly reducing time investment.
2Measurement precision
If manual data curation is performed, then accurate labels can be achieved, but the process becomes tedious and resource-intensive
Solution Approach 1:
The system achieves self-service by automatically generating ground truth labels from vehicle sensor data and operating parameters. The training data is self-labeled using the vehicle's own measurements of lane line positions, vehicle odometry, and sensor readings, eliminating tedious manual curation while maintaining high label accuracy.
Solution Approach 2:
The system replaces the mechanical process of manual data curation with an automated computational system. Instead of human annotators manually labeling features, the system uses sensor fusion and geometric calculations to automatically generate accurate labels, dramatically improving curation efficiency.
3Reliability
If more training data is collected to improve model performance, then model accuracy increases, but data collection and annotation effort increases
Solution Approach 1:
The system enables unlimited scaling of training data volume through self-service automation. Each vehicle continuously generates training data from its sensor readings and operating parameters without requiring external annotation resources. This allows accumulation of large datasets that improve model performance while avoiding the resource constraints that would normally limit data quantity.
Solution Approach 2:
The system achieves universality by creating a multi-functional data collection framework that simultaneously captures image data, sensor readings, and operating parameters for multiple training purposes. The same sensor data serves multiple functions: training object detection models, training lane detection models, and generating ground truth labels, maximizing the value extracted from each unit of data collected.
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.