Vehicle Localization Metrics Using Residual-Based Error Detection
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Solution Overview
Problem
Existing localization systems in vehicles, such as autonomous vehicles, often fail to accurately detect errors in localization components, leading to potential safety issues without providing advanced warnings or corrective actions.
Innovation Solution
A machine learned model is used to determine a localization performance metric by analyzing auxiliary localization data, including variance and residual data, to identify errors and their severity, enabling proactive actions like alerts or safe stops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localization error detection methods are used, then system complexity is reduced, but localization accuracy and error detection capability deteriorate
Solution Approach 1:
The patent introduces a machine learned model as an intermediary between the localization component and the error detection system. This model processes auxiliary localization data (variance and residual data) to generate a performance metric, enabling accurate error detection without requiring direct complex analysis of all localization parameters. The intermediary model bridges the gap between simple threshold checking and comprehensive error analysis.
2Measurement precision
If machine learned models are used to analyze auxiliary localization data, then localization accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the necessary auxiliary localization data (variance data and residual data) from the complete localization output for analysis by the machine learned model. Instead of processing all localization parameters, the system selectively extracts and analyzes only those data elements that provide insight into localization performance, reducing computational complexity while maintaining accuracy.
3Reliability
If strict error thresholds are enforced, then safety is improved, but vehicle operational continuity deteriorates
Solution Approach 1:
The patent performs preliminary analysis of auxiliary localization data to generate a performance metric before localization errors become critical. By continuously monitoring variance and residual data through the machine learned model, the system can take preventive actions (such as issuing warnings or adjusting localization parameters) before errors exceed safe thresholds, thereby maintaining both safety and operational continuity.
Data Source
AI summary
Determining a localization performance metric using sensor data is described. A vehicle may capture sensor data from one or more sensors of the vehicle. The sensor data may be input into a localization component to determine localization data (e.g., a location/orientation of the vehicle) and auxiliary location data (e.g., residual data indicating a difference between the measured location/orientation and an actual or estimated location/orientation). The auxiliary localization data (which, in some examples, may include residuals associated with various sensor modalities) may be input into a machine learned model. The machine learned model may generate the localization performance metric. The vehicle may determine an action to take based on the localization performance metric. In some examples, the action may be tied to the performance meeting or exceeding a threshold.