Multi-Modal Defect Detection for Autonomous Vehicle Behavior
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Solution Overview
Problem
Autonomous vehicles face challenges in detecting defects in their behavior due to the limitations of individual sensor modalities, which can miss anomalies, especially in complex environments, leading to sub-optimal actions.
Innovation Solution
A method and system that utilize cross-attention-based anomaly detection by comparing predicted system states from environment and system data to actual states, generating residual scores, and adjusting model parameters to minimize differences, thereby identifying defects and performing corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual sensor modalities are used for defect detection, then the system complexity is reduced, but the detection accuracy deteriorates due to missed anomalies in complex environments
Solution Approach 1:
The patent combines multiple sensor modalities (environmental sensors and system state sensors) into a unified defect detection framework. The environmental data from cameras, LiDAR, and other sensors is merged with system state data from vehicle controllers to create a comprehensive view for anomaly detection, thereby improving detection accuracy without proportionally increasing system complexity
Solution Approach 2:
The defect detection system serves multiple functions: it processes environmental data, monitors system state, generates residual scores for anomaly detection, and triggers corrective actions. This multi-functional approach allows a single system to handle diverse sensing and detection tasks, improving accuracy while managing complexity through functional integration
2Measurement precision
If multi-modal sensor data is integrated for defect detection, then the detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The defect detection process is segmented into distinct computational stages: environmental data processing, system state data processing, residual score generation, and anomaly determination. Each stage handles specific data types and computations independently, reducing the overall computational complexity while maintaining high detection accuracy through systematic data flow management
Solution Approach 2:
The system uses residual scores as feedback mechanisms to continuously monitor and compare predicted system states with actual states. This feedback loop enables the system to adaptively adjust detection thresholds and trigger corrective actions when anomalies are detected, improving accuracy while managing computational load through iterative refinement rather than exhaustive analysis
3Reliability
If residual scores from multiple models are compared to generate defect scores, then the reliability of defect detection is improved, but the processing time increases
Solution Approach 1:
The system computes residual scores from multiple prediction models but does not require all scores to be fully processed before generating a defect determination. The defect score is generated based on the comparison of residual scores, and corrective actions are triggered when the defect score exceeds a threshold, allowing the system to make timely decisions with partial processing rather than waiting for complete analysis of all data streams
Data Source
AI summary
Methods and systems for defect detection include determining a first residual score by comparing a first predicted system state, determined according to previously measured environment data, to an actual system state. A second residual score is determined by comparing a second predicted system state, determined according to previously measured system state data, to the actual system state. A defect score is generated based on a difference between the first residual score and the second residual score. An automatic action is performed responsive to a determination that the defect score indicates a defect in system behavior.