Autonomous Vehicle AI Training With GAN-Based Synthetic Scenes
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Solution Overview
Problem
The challenge in developing an artificial-intelligence system for autonomous vehicles lies in the difficulty and expense of manually labeling real-world data with pixel-level accuracy, and the limitations of simulated data introducing visual artifacts, which can lead to inadequate training scenarios for AI image processing systems.
Innovation Solution
The use of generative adversarial networks (GANs) to generate realistic simulated objects, which are then integrated into real-world scenes to create a training set with reduced artifacts, allowing for large volumes of accurately labeled data that can supplement limited real-world data and cover more scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulated images are used to provide large volumes of training data with pixel-level accuracy, then the quantity of training data is improved, but visual artifacts and distortions are introduced that reduce the reliability of the training data
Solution Approach 1:
The patent uses GANs to generate synthetic training images that copy the essential visual characteristics of real-world images while avoiding the artifacts of traditional simulation. The generative model creates photorealistic images that maintain pixel-level labeling accuracy without the visual distortions of conventional simulated environments.
Solution Approach 2:
The patent transforms the parameters of simulated images by using GANs to adjust visual characteristics such as texture, lighting, and color distribution. This allows the simulated images to match real-world image statistics while maintaining the benefits of synthetic data generation.
2Reliability
If real-world data is manually labeled with pixel-level accuracy, then the reliability of training data is improved, but the cost and time required for labeling increase significantly
Solution Approach 1:
The patent creates synthetic copies of real-world images with automatic pixel-level labels generated by the GAN system. These synthetic images replicate the labeling quality of manual annotation without requiring human annotators, thereby eliminating the time and cost overhead of manual labeling while maintaining annotation accuracy.
Solution Approach 2:
The GAN system performs self-labeling by automatically generating pixel-level annotations for synthetic images without human intervention. The generative model inherently understands the semantic content it creates, allowing it to provide accurate labels without external labeling resources.
3Productivity
If traditional simulation methods are used to generate training scenarios, then the productivity of data generation is improved, but the visual artifacts reduce the adaptability of the AI system to real-world conditions
Solution Approach 1:
The patent uses GANs to transform the visual parameters of synthetic images, adjusting texture, lighting, color, and other aesthetic properties to match real-world conditions. This maintains high productivity in data generation while significantly improving the realism and adaptability of training scenarios to actual deployment environments.
Solution Approach 2:
The GAN system copies the visual characteristics and statistical properties of real-world images into synthetic training data. This allows rapid generation of diverse training scenarios that faithfully reproduce real-world visual conditions, thereby improving both productivity and adaptability simultaneously.
Data Source
AI summary
An apparatus for building an artificial-intelligence system is provided. The apparatus accesses images of a real-world scene and generates an image of a simulated object corresponding to a real-world object using a first generative adversarial network (GAN). The apparatus inserts the image of the simulated object into the images of the real-world scene to produce images of the real-world scene including the simulated object. The apparatus applies the images of the real-world scene including the simulated object to a second GAN to remove visual artifacts thereby producing a training set of images of the real-world scene including the simulated object. The apparatus trains an artificial-intelligence algorithm using the training set of images to build the artificial-intelligence system to detect the real-world object in further images of the real-world scene and outputs the artificial-intelligence system for deployment on an autonomous vehicle.