Traffic Sign Recognition With Anomaly Detection Against Adversarial Changes
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Solution Overview
Problem
Current computer-assisted and autonomous driving systems are vulnerable to adversarial modifications of traffic signs, which can lead to misleading recognition and potentially dangerous situations, as deep neural networks used for sign recognition are susceptible to small perturbations and malicious alterations, and existing solutions rely on infrequent and unreliable human reports or maintenance.
Innovation Solution
An apparatus and method for traffic sign recognition with adversarial resilience, featuring an orchestrator in a CA/AD vehicle that receives traffic sign classifications, queries a remote or local database for reference descriptions, and uses an anomaly detector to verify the accuracy of classifications, notifying occupants and authorities of anomalies and facilitating re-training of object detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep neural networks are used for traffic sign recognition, then recognition speed and automation are improved, but vulnerability to adversarial modifications increases
Solution Approach 1:
The system implements a feedback mechanism where the anomaly detector continuously monitors traffic sign classifications and compares them against expected patterns. When anomalies are detected, the system generates alerts and can trigger retraining of the neural network, creating a closed-loop system that improves reliability while maintaining automation.
Solution Approach 2:
The patent introduces an intermediary anomaly detector component that sits between the deep neural network classifier and the final decision-making process. This intermediary layer analyzes the confidence scores and classification outputs to detect potential adversarial modifications before they affect the overall system reliability.
2Reliability
If controlled check procedures are implemented to verify traffic sign integrity, then reliability is improved, but cost and time consumption increase
Solution Approach 1:
The system performs anomaly detection periodically and continuously in the background rather than requiring manual inspection at intervals. The anomaly detector operates autonomously to monitor each traffic sign classification in real-time, providing ongoing verification without interrupting the flow of traffic or requiring dedicated inspection time.
Solution Approach 2:
The system enables self-service verification through automated anomaly detection and classification validation. The deep neural network and anomaly detector work together to automatically verify traffic sign integrity without human intervention, eliminating the need for manual controlled check procedures while maintaining high reliability.
3Measurement precision
If human experts manually inspect traffic signs for adversarial modifications, then detection accuracy is improved, but scalability and coverage are reduced
Solution Approach 1:
The patent replaces the mechanical system of human expert inspection with an automated computational system consisting of deep neural networks and anomaly detectors. This substitution maintains high detection accuracy by using sophisticated algorithms while dramatically increasing productivity by enabling simultaneous monitoring of multiple traffic signs across the entire fleet of autonomous vehicles.
Solution Approach 2:
The anomaly detection system is designed to be universal and applicable to all traffic signs encountered by autonomous vehicles. The same deep neural network and anomaly detection algorithms can identify adversarial modifications across diverse traffic sign types, locations, and conditions, enabling scalable deployment without requiring specialized human experts for each sign.
4Reliability
If frequent monitoring and retraining of object detectors are performed, then resilience to adversarial modifications is improved, but computational cost and energy consumption increase
Solution Approach 1:
The system performs preliminary anomaly detection and analysis before full retraining is necessary. The anomaly detector identifies suspicious patterns and triggers targeted updates or alerts, allowing the system to maintain high resilience while avoiding the energy-intensive process of frequent complete retraining cycles.
Solution Approach 2:
Instead of performing complete retraining of the entire deep neural network frequently, the system applies partial updates only to the specific components or layers affected by detected anomalies. This selective retraining approach maintains adversarial resilience while significantly reducing computational cost and energy consumption compared to full model retraining.
Data Source
AI summary
Apparatuses, methods and storage medium associated with traffic sign recognition, are disclosed herein. In some embodiments, an apparatus includes an orchestrator, disposed in a CA/AD vehicle, to receive a classification and a location of a traffic sign, while the CA/AD vehicle is enroute to a destination. In response, the orchestrator query a remote sign locator service or a local database on the CA/AD vehicle for a reference description of the traffic sign, determine whether the classification is correct, and output a result of the determination. The classification of the traffic sign is generated based at least in part on computer vision, and the orchestrator includes an anomaly detector to detect anomalies between the classification and the reference description, and determine whether the classification is correct based at least in part on an amount of anomalies detected. Other embodiments are also described and claimed.