Vehicle AI Testing Circuit for Robustness Checks Under Perturbations
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Solution Overview
Problem
AI-based information processing systems, particularly deep neural networks, face challenges in transparency, susceptibility to adversarial perturbations, and robustness testing, making it difficult to develop and validate them for autonomous vehicle control, especially in varying environmental conditions.
Innovation Solution
A method and device for checking AI-based information processing systems using a testing circuit that evaluates sensor data and generates test results stored in a multi-dimensional data structure, allowing for continuous assessment and updating of system robustness, including unmonitored testing methods that observe the system without interfering with its operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep neural networks are used for AI-based information processing, then the system can process sensor data and generate control outputs, but the system becomes non-transparent and susceptible to adversarial perturbations
Solution Approach 1:
The patent introduces a testing circuit as an intermediary component that sits between the sensor data input and the AI-based information processing system. This testing circuit applies adversarial perturbations to the sensor data before it reaches the neural network, enabling systematic robustness testing without interfering with the normal operation of the AI system. The testing circuit mediates between the need for robustness verification and the operational integrity of the deep neural network.
2Ease of manufacture
If neural networks are trained on synthetic data, then development costs are reduced, but the network performance on real sensor data becomes weak
Solution Approach 1:
The patent applies preliminary action by performing robustness testing with adversarial perturbations during the training phase or before deployment. The testing circuit pre-evaluates the neural network's vulnerability to various perturbations using synthetic test data, allowing developers to identify and address robustness issues before the network is deployed to process real sensor data. This preliminary robustness verification bridges the gap between synthetic training data and real-world performance.
3Reliability
If extensive robustness testing is performed on neural networks, then system reliability improves, but the complexity of testing and evaluation increases
Solution Approach 1:
The patent segments the robustness testing process into distinct functional modules within the testing circuit. The testing circuit is divided into separate components that independently generate different types of adversarial perturbations (e.g., sensor noise, weather influences, image manipulations). Each module can be independently configured and tested, allowing comprehensive robustness evaluation while maintaining manageable system complexity through modular architecture.
Data Source
AI summary
The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.