Road Scene Annotation via Machine Learning and Image Processing
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Solution Overview
Problem
Current automated automobile navigation systems face challenges in accurately generating scene attribute annotations for complex road typologies due to incomplete and inaccurate GPS data, which hinders the collection of sufficient training data for machine learning systems.
Innovation Solution
A method and system that utilize a machine learning model to receive images from imaging devices, populate attribute settings with values representing road scenes, and implement an annotation interface to adjust these values, generating a simulated overhead view of the road scene, thereby enabling accurate scene attribute annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS data is used to determine road layout information, then data collection is simplified, but accuracy and completeness of the data deteriorates
Solution Approach 1:
The patent introduces an intermediary system that combines GPS data with image processing and machine learning models. The image processing system acts as a mediator between the imprecise GPS data and the required accurate road layout information, using visual data to correct and supplement GPS-derived attributes.
Solution Approach 2:
The patent replaces reliance on mechanical GPS positioning with a computational approach using image processing and machine learning. Instead of depending on the physical accuracy of GPS signals, the system uses visual recognition and AI models to determine road layout attributes, substituting a mechanical measurement system with an intelligent processing system.
2Reliability
If machine learning systems are trained with adequate volume of road typology training data, then model accuracy improves, but data collection difficulty increases
Solution Approach 1:
The patent implements a self-service data generation system where the machine learning model processes images and automatically generates road layout annotations. The system serves itself by using the image processing capabilities to create training data without requiring manual annotation, thereby reducing data collection complexity while maintaining adequate training data volume.
Solution Approach 2:
The patent creates synthetic copies of real-world road scenes through image processing and simulation. By generating virtual representations of road typologies from captured images, the system can create multiple training examples from a single real-world observation, effectively multiplying the training data volume without increasing physical data collection effort.
3Ease of manufacture
If map data is used for navigation, then existing infrastructure is utilized, but data completeness and currency deteriorates
Solution Approach 1:
The patent transforms static map data into a dynamic system that continuously updates road layout information based on real-time image processing. Instead of relying on fixed, potentially outdated map databases, the system dynamically generates and updates road typology annotations from current visual observations, ensuring data currency and completeness.
Solution Approach 2:
The patent performs preliminary processing of images to extract road layout information before final map data is generated. By pre-processing visual data to identify and annotate road attributes, the system prepares comprehensive and updated map information in advance, ensuring completeness before the data is integrated into navigation systems.
Data Source
AI summary
Systems and methods for road typology scene annotation are provided. A method for road typology scene annotation includes receiving an image having a road scene. The image is received from an imaging device. The method populates, using a machine learning model, a set of attribute settings with values representing the road scene. An annotation interface is implemented and configured to adjust values of the attribute settings to correspond with the road scene. Based on the values of the attribute settings, a simulated overhead view of the respective road scene is generated.