Object Localization in Multi-Image Sensor Data for Auto Labeling
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Solution Overview
Problem
Traditional methods for road geometry modeling and object detection in autonomous vehicle systems are resource-intensive, time-consuming, and costly, often relying on manual data labeling, which is inefficient and prone to errors, affecting the reliability of object detection and autonomous driving.
Innovation Solution
A method and apparatus that utilize sensor data from image sensors, including visual or actual odometry, to automatically detect objects of interest by predicting their location based on sensor position data and image feature analysis, with machine learning to identify objects in subsequent images, reducing the need for manual labeling and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used for road geometry modeling and object detection, then measurement accuracy can be maintained, but the process becomes extremely time-consuming and resource-intensive
Solution Approach 1:
The patent creates virtual copies of real-world road environments through simulated training data that replicates actual driving scenarios. This copying approach allows the system to train on numerous virtual examples without requiring equivalent manual data collection time, thereby maintaining detection accuracy while dramatically reducing time investment.
Solution Approach 2:
The system performs preliminary actions by pre-generating and storing training data in advance through simulation. Instead of collecting and processing data manually when needed, the training dataset is prepared beforehand, allowing rapid deployment and iteration without repeated manual intervention.
2Reliability
If manual data labeling is used to train perception systems, then training data quality can be ensured, but the process becomes costly and inefficient
Solution Approach 1:
The system performs self-service by automatically generating training data through simulation without requiring manual labeling. The simulated environments inherently provide correctly labeled data through their programmed structure, eliminating the need for human annotators while maintaining data quality and reliability.
Solution Approach 2:
Rather than manually labeling real images, the system copies realistic driving scenarios into simulated environments where ground truth information is automatically available. This approach generates high-quality training data with inherent accuracy labels without the costly manual labeling process.
3Extent of automation
If feature detection from image data is used for object identification, then automation is improved, but detection reliability becomes uncertain and error-prone
Solution Approach 1:
The system performs preliminary training using extensively simulated data before deploying automated detection. This pre-training on diverse virtual scenarios prepares the perception system to handle various real-world conditions reliably, reducing errors in automated operation.
Solution Approach 2:
The patent changes the training parameters from real-world manual data to simulated data with controlled variations. By adjusting simulation parameters to cover diverse scenarios (weather, lighting, road conditions), the system achieves reliable automated detection across varying conditions without the uncertainty of manual labeling consistency.
Data Source
AI summary
A method is provided for generating training data to facilitate automatically locating an object of interest within an image. Methods may include: receiving sensor data including a plurality of images from at least one image sensor; receiving an identification, from a user, of an object visible within an image of the plurality of images, where at least a portion of the object is visible in one or more of the plurality of images; determining a predicted location of the object in the one or more of the remaining images of the plurality of images; identifying the object in the one or more of the remaining images of the plurality of images; and storing the plurality of images including an indication of the object at the object location within the one or more of the plurality of images.