Semantic Map Image Generation for Rare Traffic Scenario Training
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Solution Overview
Problem
Existing image classifiers for autonomous vehicles struggle to learn optimal classification of rare traffic situations due to underrepresentation in training data and require significant manual labeling, leading to gaps in knowledge for infrequent but critical scenarios.
Innovation Solution
Generate realistic images from semantic maps using a generator network, combining a noise tensor with a map tensor to create variable and semantically meaningful images, enhancing training data with rare scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real training images are captured from a wide range of traffic situations, then the image classifier can learn optimal classification, but acquiring the training images is comparatively difficult and costly
Solution Approach 1:
The patent uses a generator network to synthesize realistic training images from semantic maps, creating copies of real-world traffic scenarios without physically capturing them. This allows generating unlimited training data with rare situations by programmatically creating semantic maps and their corresponding realistic images, eliminating the need for difficult and costly real-world data collection while maintaining classification accuracy.
2Reliability
If training images are captured in rare traffic situations, then the image classifier can learn correct classification thereof, but these situations are numerically underrepresented in the data set
Solution Approach 1:
The patent performs preliminary action by pre-defining semantic maps that include rare traffic situations before generating training images. By programmatically creating semantic maps with specific rare scenarios (e.g., unusual traffic configurations, edge cases) and then generating multiple realistic image variants from each map, the system ensures adequate representation of rare situations in the training dataset, allowing the classifier to learn correct classification for these underrepresented cases.
3Measurement precision
If manual work is performed to label training images, then ground truth is obtained for training, but a lot of manual work is required
Solution Approach 1:
The patent implements self-service by having the generator network automatically generate both the training images and their corresponding labels simultaneously from semantic maps. The semantic maps serve as structured representations that directly provide ground truth information (object locations, types, and attributes) without requiring manual annotation. This automated process eliminates time-consuming manual labeling while maintaining high labeling accuracy, as the generator inherently knows the correct labels based on the semantic map input.
4Reliability
If realistic images are generated with specified semantic meanings, then comprehensive training of difficult scenarios is achieved, but the generator requires training and configuration
Solution Approach 1:
The patent segments the image generation process into distinct modular components: semantic map creation, noise tensor generation, and image synthesis through the generator network. Each component has a specific function and can be independently trained and configured. The semantic maps are divided into multiple channels representing different object types and attributes, allowing targeted generation of specific rare scenarios. This segmentation reduces overall system complexity by making each part manageable and independently optimizable while achieving comprehensive training coverage.
Data Source
AI summary
A method for generating images from a semantic map, which assigns to each pixel of the images a semantic meaning of an object to which this pixel belongs. The semantic map is provided as a map tensor comprising channels which each indicates all the pixels of the images to be generated, to which the semantic map assigns a specific semantic meaning; a set of variable pixels of the images to be generated is provided, which are to vary from one image to the next; using values taken from a random distribution, a noise tensor with channels is generated, those values of the noise tensor which relate to the set of variable pixels being reused for each image to be generated; the channels of the map tensor are merged with the channels of the noise tensor to yield an input tensor, which is mapped by a trained generator onto an image.