Traffic Image Domain Transfer for ECU Training Across Weather
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Solution Overview
Problem
Existing methods for training vehicle electronic control units (ECUs) using images are inefficient and costly, particularly in adapting to varying environmental conditions such as lighting and weather, requiring extensive manual labeling of additional datasets for each condition.
Innovation Solution
A domain transfer diffusion model is employed to transform traffic images from one environmental condition to another, applying a denoising neural network to add and remove noise, allowing image labels to be transferred between conditions, thereby generating and labeling images efficiently without the need for new dataset collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual labeling of additional datasets is performed for each environmental condition, then training data coverage is improved, but time and cost increase significantly
Solution Approach 1:
The patent applies copying by transferring image labels from source environmental conditions to target environmental conditions through domain transfer diffusion models. Instead of manually labeling new datasets for each condition, the system generates synthetic labeled images by transforming existing labeled images to different domains (weather, lighting, time of day conditions), thereby copying label information across environmental variations.
Solution Approach 2:
The patent utilizes parameter changes by modifying environmental parameters (weather conditions, lighting, time of day) through domain transfer diffusion models. The system changes the environmental parameters of existing images to generate new training data covering various conditions without requiring manual relabeling, thus adapting the training data to diverse conditions efficiently.
2Adaptability or versatility
If manual labeling of additional datasets is performed for each environmental condition, then training data coverage is improved, but cost increases significantly
Solution Approach 1:
The patent applies copying by transferring image labels from source environmental conditions to target environmental conditions through domain transfer diffusion models. Instead of manually labeling new datasets for each condition, the system generates synthetic labeled images by transforming existing labeled images to different domains (weather, lighting, time of day conditions), thereby copying label information across environmental variations.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically generate its own training data through domain transfer diffusion models. The system autonomously transforms existing labeled images into diverse environmental conditions without human intervention, making the data generation process self-sufficient and eliminating the need for expensive manual labeling services.
3Adaptability or versatility
If extensive manual labeling is performed to cover all environmental conditions, then ECU adaptability is improved, but productivity decreases
Solution Approach 1:
The patent applies copying by transferring image labels from source environmental conditions to target environmental conditions through domain transfer diffusion models. Instead of manually labeling new datasets for each condition, the system generates synthetic labeled images by transforming existing labeled images to different domains (weather, lighting, time of day conditions), thereby copying label information across environmental variations.
Solution Approach 2:
The patent utilizes parameter changes by modifying environmental parameters (weather conditions, lighting, time of day) through domain transfer diffusion models. The system changes the environmental parameters of existing images to generate new training data covering various conditions without requiring manual relabeling, thus adapting the training data to diverse conditions efficiently.
4Measurement precision
If new datasets are collected for each environmental condition, then training accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies copying by transferring image labels from source environmental conditions to target environmental conditions through domain transfer diffusion models. Instead of manually labeling new datasets for each condition, the system generates synthetic labeled images by transforming existing labeled images to different domains (weather, lighting, time of day conditions), thereby copying label information across environmental variations.
Solution Approach 2:
The patent implements universality by creating a single domain transfer diffusion model system that can generate training data for multiple environmental conditions simultaneously. The model serves multiple functions by adapting images to various weather conditions, lighting scenarios, and time of day settings, eliminating the need for separate data collection systems for each condition.
Data Source
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AI summary
Disclosed is a vehicle testing system (100, 300) comprising a memory (110) storing machine executable instructions (120) and a domain transfer diffusion model (122). The execution of the machine executable instructions causes a computational system (104) to: receive (200) a first sequence of traffic images (124) descriptive of the first set of environmental conditions with respectively assigned image labels (126); generate (202) a second sequence of traffic images (128) descriptive of the second set of environmental conditions as the output of a domain transfer diffusion model (122) by inputting the first sequence of traffic images into the domain transfer diffusion model; copy (204) the image labels from the first sequence of traffic images to corresponding images of the second sequence of traffic images; and provide (206) the second sequence of traffic images (130) with the image labels.