Autonomous Vehicle Localization Error Estimation With Metadata Feedback
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Solution Overview
Problem
Existing localization systems in autonomous vehicles face inaccuracies in location uncertainty estimates due to varying environmental conditions and localizer types, leading to under or overapproximated location error estimates.
Innovation Solution
Collect and aggregate location and environmental metadata from multiple localization instances to compute revised location error estimates, using statistical methods to improve uncertainty accuracy for each localizer type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uncertainty estimates from individual localizers are used directly, then the localization process is simple, but the location error estimates are inaccurate due to under or over approximation
Solution Approach 1:
The patent combines uncertainty estimates from multiple localizers (GPS, IMU, camera-based, LiDAR-based) into a unified location error estimate. By merging data from different sensor types and processing them through sensor fusion algorithms, the system achieves more accurate localization than any single localizer could provide independently, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system uses actual location comparisons (ground truth data) to compute location errors and feed this information back to refine uncertainty estimates. This feedback mechanism allows the system to learn from past performance and continuously improve the accuracy of location error estimates, transforming a static estimation process into a dynamic, self-correcting system.
2Measurement precision
If multiple localizers are aggregated to improve accuracy, then location error estimates become more accurate, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary computations by pre-processing sensor data and pre-computing uncertainty estimates from each localizer before the final fusion step. By preparing data in advance and organizing it into standardized formats, the system reduces the computational burden during real-time operation, allowing accurate multi-localizer aggregation without excessive computation time.
3Reliability
If uncertainty estimates are refined through aggregation and environmental metadata, then localization accuracy improves in challenging environments, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies different processing strategies and uncertainty estimation methods tailored to specific environmental conditions and localizer types. Rather than using a uniform approach for all scenarios, the system adapts its processing to local requirements—applying appropriate weighting and fusion algorithms based on the specific characteristics of each localizer and environmental context, thereby improving reliability without uniformly increasing complexity across all operations.
Data Source
AI summary
The disclosed technology provides solutions for improving the accuracy of localization error estimates and in particular, provides methods for improving the accuracy of error estimates associated with individual localizers. A method of the disclosed technology can include steps for receiving a first location error estimate, corresponding with a first localizer of a first autonomous vehicle (AV), receiving a second location error estimate, corresponding with a second localizer of the first AV, and associating the first location error estimate and the second location error estimate with first location metadata and first environmental metadata corresponding with the first AV. The method can further include steps for determining a location error variance for the first localizer, based on the first location metadata, and the first environmental metadata. Systems and machine-readable media are also provided.