Synthetic Image Generator for Autonomous Driving Training Data
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Solution Overview
Problem
Conventional methods for training image classifiers for autonomous driving face challenges in obtaining representative training data, especially for rare traffic situations, and require significant manual effort for labeling, while existing synthetic data generation methods may not adequately address these issues.
Innovation Solution
A method utilizing a discriminator and generator, based on deep neural networks, to characterize semantic segmentation and style properties of input images, allowing for the generation of synthetic training data that can enhance the training of image classifiers, including the use of encoder-decoder devices and feedback mechanisms to improve training efficiency and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real training images are collected from various traffic situations, then the training data represents real-world scenarios, but obtaining sufficient data for rare traffic situations is difficult and expensive
Solution Approach 1:
The patent uses a generator network to create synthetic training images that copy the essential characteristics of real traffic scenes. The generator produces photorealistic images of rare traffic situations without requiring physical collection, thereby resolving the contradiction between data representativeness and acquisition efficiency
Solution Approach 2:
The system employs self-service through automated synthetic data generation. The generator and discriminator networks work together in an adversarial framework where the generator automatically creates diverse training samples without human intervention, eliminating the need for manual data collection and labeling efforts
2Productivity
If synthetic training data is generated using GANs, then data acquisition efficiency improves, but the training process becomes complex and computationally demanding
Solution Approach 1:
The patent segments the complex training process into distinct functional components: an encoder that extracts features, a generator that creates images, and a discriminator that validates realism. This segmentation allows each component to be optimized independently and simplifies the overall training methodology
Solution Approach 2:
The adversarial feedback mechanism between generator and discriminator provides automatic quality assessment during training. The discriminator's feedback signals guide the generator to improve image realism, creating a self-regulating training process that reduces the need for complex external validation procedures
3Measurement precision
If manual labeling of training images is performed, then accurate ground truth is obtained, but significant manual work and time are required
Solution Approach 1:
The system copies semantic information from simplified source data into photorealistic generated images. The encoder processes simple semantic maps and the generator reproduces realistic images with corresponding ground truth automatically embedded, eliminating manual labeling while preserving accuracy
Solution Approach 2:
The patent replaces the mechanical process of manual labeling with an automated neural network pipeline. The encoder-generator system automatically generates both images and their corresponding ground truth labels through computational processes, substituting human manual work with algorithmic automation
Data Source
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AI summary
Method, for example a computer-implemented method, for processing image data characterizing, for example at least, an input image, comprising: providing the input image, determining first information that characterizes a semantic segmentation of the input image by means of a discriminator, determining second information that is different from the first information and that characterizes properties associated with the input image by means of the discriminator.