Driver Assistance Sensor Verification Against Adversarial Perturbations
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Solution Overview
Problem
Deep neural networks used in driver assistance systems are susceptible to adversarial perturbations, making them unreliable for real-world applications, as they cannot distinguish between natural and adversarial changes in sensor data, leading to poor performance across different environmental conditions.
Innovation Solution
A method that verifies sensor data by comparing them to previously recorded data stored in a database, identifying known perturbations and distinguishing between natural and adversarial changes, ensuring only reliable data is used for vehicle control in partially or fully automated driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep neural networks are used to analyze sensor data in driver assistance systems, then the automation level and driving functionality are improved, but the system becomes susceptible to adversarial perturbations and unreliable
Solution Approach 1:
The patent applies preliminary action by training the neural network in advance with adversarially perturbed sensor data. The verification unit pre-generates perturbed training data and uses it to train the neural network before deployment, enabling the system to learn robust features that are invariant to adversarial perturbations. This preliminary training with worst-case scenarios prepares the network to reliably distinguish between natural and adversarial changes in sensor data.
2Speed
If the neural network processes all sensor data without verification, then the processing speed is maintained, but the system cannot distinguish between natural and adversarial changes leading to incorrect control decisions
Solution Approach 1:
The patent segments the sensor data processing into distinct functional units: a verification unit that detects adversarial perturbations and a neural network evaluation unit that performs the actual analysis. The verification unit acts as a preliminary filter that segments out malicious data before it reaches the main processing pipeline, allowing the system to maintain high processing speed for legitimate data while achieving high detection accuracy through specialized verification mechanisms.
3Ease of operation
If adversarial perturbations are not detected and filtered, then the system operation is simple and fast, but the robustness to environmental changes and adversarial attacks is poor
Solution Approach 1:
The patent introduces a verification unit as an intermediary component between the sensor data input and the neural network evaluation. This intermediary layer provides robustness by detecting and filtering adversarial perturbations before they can corrupt the decision-making process. The verification unit acts as a mediator that protects the simplicity of the main processing pipeline while adding the necessary complexity only where needed for security and robustness.
Data Source
AI summary
A method for operating a driver assistance system of a vehicle is disclosed, wherein sensor data are recorded from the surroundings of the vehicle, the recorded sensor data are verified, the verified sensor data are analyzed by a neural network and analyzed sensor data are generated. Based on the analyzed sensor data, control data are generated for controlling the vehicle. During verification of the sensor data, at least first sensor data, which were recorded at a first, earlier point in time, are compared with second sensor data, which were recorded at a second, later point in time, the result of the comparison is cross-checked with a database in which data on perturbations to input data of a neural network are stored, wherein it is checked whether the second sensor data were generated at least in part by a perturbation to the first sensor data that is stored in the database.