Component Verification Using Ground Truth for AV Disengagement Analysis
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Solution Overview
Problem
Autonomous vehicle systems face challenges in accurately determining the root cause of failures, particularly when perception data is uncertain, making it difficult to distinguish between perception and prediction component issues, which can lead to disengagement events and potential safety hazards.
Innovation Solution
A component verification system is implemented to analyze log data from autonomous vehicle operations, using ground truth data to simulate scenarios and determine the cause of disengagement events, thereby attributing errors to specific components and suggesting improvements to reduce future occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is captured and perception processing is performed, then perception data is generated to detect objects in the environment, but the perception data contains a degree of uncertainty that makes it difficult to determine the root cause of failures
Solution Approach 1:
The patent introduces an intermediary verification system that acts as a mediator between the perception component and prediction component. This verification system receives outputs from both components, compares them against ground truth data, and determines which component is responsible for discrepancies. The intermediary system resolves the uncertainty by providing a structured comparison framework that attributes errors to specific components rather than treating all perception data as equally unreliable.
Solution Approach 2:
The patent implements a feedback mechanism where the verification system continuously monitors and compares perception data against ground truth, then feeds back information about which components are generating errors. This feedback loop allows the system to learn from discrepancies and improve component reliability over time, addressing the fundamental issue of determining root causes by creating a closed-loop verification process.
2Reliability
If the autonomous operation system attempts to determine the root cause of failures, then component reliability can be improved, but the complexity of analyzing perception data and prediction outputs increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the verification process into distinct modular components: a perception component that generates perception data, a prediction component that generates predicted outputs, and a verification system that compares these against ground truth. Each component has a specific function, and the verification system segments the analysis by separately evaluating perception accuracy and prediction accuracy. This modular segmentation reduces overall system complexity by making each component's role clear and manageable.
Solution Approach 2:
The verification system acts as an intermediary that simplifies the complex task of root cause analysis by providing a structured comparison framework. Rather than requiring direct complex analysis between perception and prediction components, the intermediary verification system mediates the comparison against ground truth data, thereby reducing the analytical burden and system complexity while maintaining reliable failure determination.
3Productivity
If log data is collected and analyzed to determine component errors, then targeted improvements can be made to reduce disengagement events, but the time and computational resources required for analysis increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing log data as it is collected, rather than performing comprehensive analysis only when needed. The verification system continuously compares perception and prediction outputs against ground truth data in real-time, pre-identifying errors and their sources. This preliminary analysis reduces the time required for later investigations because the data is already processed, categorized, and ready for quick review, thereby improving productivity without proportionally increasing analysis time.
Data Source
AI summary
Techniques are disclosed for component verification for complex systems. The techniques may include receiving log data, obtaining ground truth data based on the log data and determining an outcome at least in part by simulating a prediction by a prediction component based on the log data and the ground truth data. The techniques may further include simulating a second prediction by the prediction component based on the ground truth data, determining whether the second prediction resulted in the negative outcome of the scenario and determining the disengagement event is attributable to a perception component of the autonomous operation system at least partly in response to determining the second prediction based on the ground truth data did not result in the negative outcome.