Canonical Driving Image Translation to Reduce Highway Domain Shift
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Solution Overview
Problem
Existing vision-based autonomous driving models face challenges in generalizing to new datasets due to domain shift and are suboptimal in performance, with GAN-based models suffering from unstable training, mode collapse, and resource-intensive image generation with undesired hallucinated details.
Innovation Solution
The DU-drive model transforms real images into canonical virtual representations using a generative adversarial neural network, eliminating nuisance background information and focusing on critical prediction details, thereby predicting autonomous device commands effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If GAN-based domain adaption is used to transform virtual images to real images, then realism of generated images is improved, but training stability deteriorates due to mode collapse and unstable training
Solution Approach 1:
The patent introduces a canonical representation as an intermediary form between real and virtual images. Instead of directly transforming virtual images to real images (which causes instability), the system first converts both to a simplified canonical form that removes nuisance details, then performs domain adaption in this stable intermediate space. This mediator approach resolves the contradiction by avoiding direct confrontation between conflicting image distributions.
Solution Approach 2:
The patent extracts and removes nuisance information (background details, brand logos, irrelevant scene elements) from images to create a canonical representation. By taking out these problematic elements that cause domain shift and training instability, the system achieves more stable training while maintaining essential predictive information needed for driving decisions.
2Measurement precision
If real-world data collection is performed to improve model performance, then prediction accuracy is improved, but data collection cost and time increase prohibitively
Solution Approach 1:
The patent creates simplified canonical copies of real driving images that retain essential predictive information while removing nuisance details. These canonical representations serve as efficient proxies for full-resolution real images, enabling the model to learn from reduced data representations that require less collection, storage, and processing time while maintaining prediction accuracy.
Solution Approach 2:
Instead of starting with real images and trying to make them more useful, the patent inverts the approach by first creating simplified canonical representations and then using these as the foundation for domain adaption. This inversion allows the system to work with less complex data from the outset, reducing the burden of data collection and processing while achieving the same predictive goals.
3Measurement precision
If intermediate representations are added to vanilla end-to-end models, then model performance is improved, but device complexity and annotation cost increase
Solution Approach 1:
The patent creates a universal canonical representation that serves multiple functions simultaneously: it acts as a simplified input for prediction, a stable intermediate for domain adaption, and a normalized form that reduces variability across different datasets. This single multi-functional representation eliminates the need for separate processing pipelines for different image types, reducing overall system complexity while maintaining performance benefits.
Data Source
AI summary
An unsupervised real to virtual domain unification model for highway driving, or DU-drive, employs a conditional generative adversarial network to transform driving images in a real domain to their canonical representations in the virtual domain, from which vehicle control commands are predicted. In the case where there are multiple real datasets, a real-to-virtual generator may be independently trained for each real domain and a global predictor could be trained with data from multiple real domains. Qualitative experiment results show this model can effectively transform real images to the virtual domain while only keeping the minimal sufficient information, and quantitative results verify that such canonical representation can eliminate domain shift and boost the performance of control command prediction task.