Generative Models for Autonomous Driving Testing
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Solution Overview
Problem
Current deep learning models, particularly deep neural networks, face challenges in generalizing beyond their training data scope and are susceptible to adversarial examples, making their behavior unpredictable in rare events and unseen cases, which is critical for ensuring safe and reliable autonomous driving systems.
Innovation Solution
The use of generative machine learning models, specifically disentangled representation learning with Variational Auto-Encoders (VAE), to systematically generate realistic synthetic images that improve data coverage and reveal weaknesses in AI models by modifying existing images, allowing for comprehensive testing without real-world exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for autonomous driving, then performance in limited training situations is improved, but reliability in rare events and unseen cases deteriorates
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data before deployment. Adversarial examples are created in advance to augment the training dataset, enabling the model to learn from potential failure scenarios beforehand. This preliminary data preparation improves reliability in rare events without compromising recognition accuracy in common situations.
2Productivity
If deep learning models are deployed, then performance in training scenarios is improved, but susceptibility to adversarial examples increases
Solution Approach 1:
The system converts the harmful effect of adversarial examples into a beneficial training mechanism. By deliberately introducing adversarial perturbations during training, the model learns to recognize and resist these attacks. This transforms the previously harmful susceptibility into a strength, improving robustness while maintaining processing speed.
3Adaptability or versatility
If more training data is collected, then generalization capability is improved, but data coverage for rare events remains insufficient
Solution Approach 1:
The system creates synthetic copies of training data through adversarial example generation. Instead of relying solely on physical data collection, virtual copies of rare scenarios are generated computationally. This copying approach enables comprehensive coverage of rare events without requiring extensive real-world data collection, maintaining adaptability while expanding data coverage.
Data Source
AI summary
A system includes a display device, a memory configured to store a visual analysis application and image data including a plurality of images including detectable objects; and a processor, operatively connected to the memory and the display device. The processor is configured to execute the visual analysis application to learn generative factors from objects detected in the plurality of images, visualize the generative factors in a user interface provided to the display device, receive grouped combinations of the generative factors and values to apply to the generative factors to control object features, create generated objects by applying the values of the generative factors to the objects detected in the plurality of images, combine the generated objects into the original images to create generated images, and apply a discriminator to the generated images to reject unrealistic images.