Lane And Traffic AI Model Security With Signature-Verified Learning
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Solution Overview
Problem
ADAS and autonomous vehicles are susceptible to cyberattacks due to their reliance on AI models trained with unverified data, which can lead to false positives and negatives, posing serious safety risks.
Innovation Solution
Implementing signature verification using private and public keys to ensure that training and continuous learning data for AI models in lane and traffic management comes from verifiable sources, blocking unauthorized data to secure the models during training, testing, and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are trained with unverified data from multiple data sources, then the model's adaptability and learning capability are improved, but the system becomes susceptible to cyberattacks and data integrity is compromised
Solution Approach 1:
The patent applies preliminary action by implementing signature verification mechanisms before data is used for training the AI model. Data sources are pre-authenticated and their data is signed with private keys before being fed into the training pipeline, preventing malicious data injection at the source level.
Solution Approach 2:
The patent introduces an intermediary verification layer between data sources and the AI model training process. This intermediary system uses public key infrastructure to verify data authenticity, acting as a mediator that filters out untrusted data while allowing legitimate data to pass through for model training.
2Reliability
If signature verification is implemented on all training data, then data integrity and security are improved, but the complexity of the data processing pipeline increases
Solution Approach 1:
The patent applies self-service by making data sources self-verify through digital signatures. Each data source independently signs its own data with its private key, and the verification system automatically checks these signatures without requiring manual intervention, reducing operational complexity while maintaining security.
Solution Approach 2:
The patent changes the parameter of data authentication from manual verification to automated cryptographic verification. By transforming the authentication mechanism into a mathematical signature verification process, the system achieves high reliability without proportionally increasing operational complexity.
3Reliability
If continuous monitoring and verification of deployed AI models is performed, then the model's reliability and safety are improved, but the computational resources and time required are increased
Solution Approach 1:
The patent extracts the heavy computational burden of signature verification from the real-time inference path and places it in the data ingestion and model retraining pipelines. This separation allows continuous monitoring with minimal impact on operational energy consumption, as verification occurs during scheduled updates rather than during active model deployment.
Solution Approach 2:
The patent implements periodic verification and retraining cycles instead of continuous real-time verification of all incoming data during model operation. Data is verified and used for retraining at scheduled intervals, reducing computational overhead while maintaining model safety and reliability through regular updates.
Data Source
AI summary
A system and method to secure lane and traffic management AI models used in an advanced driver assistance system. (ADAS) or in an autonomous vehicle. Only lane and traffic management data recognized from a verifiable source is allowed for testing and training the AI model. All other data is blocked. Controllable parameters of the AI model are encrypted prior to deployment. After deployment, the controllable parameters are decrypted and only signature verified dynamic learning data is applied to the AI model for continuous learning.