GAN Synthetic Image Generation for Autonomous Vehicle Training Data
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Solution Overview
Problem
Existing autonomous vehicle technologies face challenges in accurately navigating and avoiding objects in varying lighting and weather conditions due to the lack of sufficient training data, leading to inconsistencies in image recognition and vehicle operation.
Innovation Solution
A method using a generative adversarial network (GAN) to generate photorealistic synthetic images, combined with stereo visual odometry, to train a deep neural network (DNN) for object detection and vehicle path determination, ensuring temporal consistency and accurate 3D pose estimation, thereby enhancing the vehicle's ability to operate safely in diverse environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world training data is used to train the deep neural network, then the training data reflects actual driving conditions, but the quantity and variety of training data are insufficient to cover all lighting and weather conditions
Solution Approach 1:
The patent creates synthetic copies of real-world driving scenes through 3D rendering engines that simulate various lighting and weather conditions. These synthetic image sequences replicate actual driving environments without requiring physical capture of every possible condition, thereby expanding training data quantity while maintaining realism through photorealistic rendering techniques.
Solution Approach 2:
The system varies multiple parameters in the synthetic scene generation process including lighting conditions (sun position, intensity, color temperature), weather conditions (precipitation type and intensity, fog density), and temporal parameters (time of day, season). By systematically changing these parameters, the system generates diverse training data covering all possible driving conditions from a limited set of base scenes.
2Quantity of substance
If synthetic images are generated to expand training data, then the quantity and variety of training scenarios increase, but the images may lack photorealism and temporal consistency
Solution Approach 1:
The system uses photorealistic rendering techniques that copy the visual characteristics of real-world scenes including accurate lighting models, material properties, and atmospheric effects. By replicating the optical physics of real camera systems and scene geometry, the synthetic images achieve photorealism that convinces the neural network they are processing authentic driving data.
Solution Approach 2:
The system pre-computes and stores 3D scene representations, camera poses, and environmental parameters before generating image sequences. This preliminary preparation ensures temporal consistency by maintaining accurate spatial relationships and physical constraints across all generated frames, preventing temporal artifacts that would otherwise compromise image realism.
3Productivity
If a deep neural network is trained on limited real data, then the training process is faster and requires less computational resources, but the vehicle's ability to operate safely in diverse environments is compromised
Solution Approach 1:
The system performs comprehensive scene generation and data augmentation in advance, creating extensive synthetic training datasets that cover all possible driving conditions before neural network training begins. This preliminary data preparation eliminates the need for slow, resource-intensive training on diverse real-world data while ensuring the network learns from comprehensive environmental variations.
Solution Approach 2:
The system creates synthetic copies of diverse driving environments through 3D rendering, allowing the neural network to be trained on unlimited variations of driving scenes without requiring proportional increases in real-world data collection or training computational resources. The synthetic data copying process is computationally efficient compared to capturing and processing equivalent real-world data.
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate two or more stereo pairs of synthetic images and generate two or more stereo pairs of real images based on the two or more stereo pairs of synthetic images using a generative adversarial network (GAN), wherein the GAN is trained using a six-axis degree of freedom (DoF) pose determined based on the two or more pairs of real images. The instructions can further include instructions to train a deep neural network based on a sequence of real images and operate a vehicle using the deep neural network to process a sequence of video images acquired by a vehicle sensor.