AI-Generated Test Data for Image-Dependent Control Algorithms
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Solution Overview
Problem
Testing complex image processing algorithms, especially in fields like advanced driver assistance and autonomous vehicle control, is challenging due to the complexity of designing comprehensive tests and the need for human intervention, which limits the ability to generate diverse and realistic test data sets.
Innovation Solution
A method using trained artificial intelligence, specifically long short-term memory networks and generative adversarial networks, to generate synthetic test data sets that mimic real image and sensor data, allowing for automated and comprehensive testing of algorithms by producing diverse and realistic scenarios without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test scenario production is used with high human intervention, then test data quality and realism are improved, but testing productivity and automation capability deteriorate
Solution Approach 1:
The patent uses generative adversarial networks (GANs) to create synthetic test data that copies the statistical properties and realistic characteristics of real-world data. The generative model learns from real test data and generates synthetic samples that preserve the essential patterns and distributions, enabling automated testing while maintaining data quality and realism without manual intervention
Solution Approach 2:
The patent replaces manual mechanical processes of test data creation with an automated AI-based generative system. Instead of human experts manually constructing test scenarios, the system uses trained neural networks to automatically generate synthetic test data, substituting human cognitive work with computational processes that can scale efficiently
2Reliability
If comprehensive testing coverage is pursued for complex algorithms, then algorithm reliability is improved, but test design complexity and time consumption worsen
Solution Approach 1:
The patent performs preliminary action by pre-training generative models on comprehensive real-world test data before actual testing campaigns. The generative adversarial networks are trained in advance to learn the full distribution of realistic test scenarios, enabling rapid generation of comprehensive test coverage during execution without time-consuming manual test design for each testing campaign
Solution Approach 2:
The system enables self-service by allowing the generative model to automatically generate diverse test scenarios without human intervention. Once trained, the model independently produces synthetic test data covering various edge cases and scenarios, eliminating the need for continuous human involvement in test design and significantly reducing test preparation time
3Adaptability or versatility
If diverse test scenarios are generated manually, then test coverage is improved, but ease of operation and automation integration worsen
Solution Approach 1:
The patent implements universality by creating a single generative adversarial network system that can produce diverse test scenarios across multiple domains and applications. The trained generative model serves multiple functions: generating images, sensor data, scenarios, and ground truth annotations simultaneously, and can be applied to different algorithm types (object detection, segmentation, tracking) without requiring separate manual test design processes for each
Data Source
AI summary
The invention relates to a method for producing a test data record (12) for an algorithm (1), comprising the following steps: 1.1 a trained artificial intelligence (8) is provided. 1.2 the artificial intelligence (8) is stimulated, particularly using a random signal (11) and/or using a quasi linearity property for visual concepts, and 1.3 the artificial intelligence (8) produces at least one test data record (14), which comprises image data (15) and action regions (16) associated with the image data (1g) and/or sensor data and action regions associated with the sensor data. The invention furthermore relates to a method for operating a system for the automated, image-dependent control of a device and an apparatus for carrying out the aforementioned method. Finally, the invention relates to a control system for a device which comprises such an apparatus, and a computer program product, a computer-readable medium, the production of a data storage device and the use of an artificial intelligence (8).