Sensor Error Prediction for Map-Aided Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional localization methods in autonomous driving, such as GPS, face significant errors in urban environments due to multipath reflections, leading to inaccurate vehicle positioning, which is insufficient for safe autonomous operation, especially in areas requiring centimeter-level accuracy.
Innovation Solution
A machine learning model is trained on ground truth sensor error data to predict sensor errors by processing sensor data and map data, enabling the calculation of predicted sensor errors, which can be used as priors for localization, reducing the search space and improving positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GPS sensors are used for localization, then the system is simple and easy to operate, but the measurement precision deteriorates significantly in urban environments due to multipath reflections
Solution Approach 1:
The patent introduces map data as an intermediary element between the GPS sensor and the localization result. By comparing GPS coordinates with pre-stored map data (road networks, building footprints, terrain features), the system can identify and correct multipath reflection errors. The map data acts as a reference framework that mediates the unreliable GPS measurements, enabling centimeter-level accuracy without replacing the GPS hardware itself.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and storing detailed map data before the actual localization task. The map data including road networks, building boundaries, and terrain features is prepared in advance and stored in the system. This preliminary preparation allows the localization algorithm to quickly compare real-time GPS readings against the pre-established geographic framework, significantly improving response time and accuracy without adding computational complexity during real-time operation.
2Measurement precision
If GPS sensor error is not corrected, then the system operates quickly and simply, but the localization accuracy deteriorates to meter-level errors
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing detailed map data before the actual localization task. The map data including road networks, building boundaries, and terrain features is prepared in advance and stored in the system. This preliminary preparation allows the localization algorithm to quickly compare real-time GPS readings against the pre-established geographic framework, significantly improving response time and accuracy without adding computational complexity during real-time operation.
Solution Approach 2:
The patent replaces the purely computational error correction approach with a data-driven methodology. Instead of using complex real-time calculations to estimate and correct GPS errors, the system substitutes this with a comparison against pre-collected ground truth data stored in map databases. This substitution transforms the real-time computational problem into a data retrieval and comparison task, dramatically reducing processing time while maintaining high accuracy.
Data Source
AI summary
An approach is provided for providing predictive classification of sensor error. The approach involves, for example, receiving sensor data from at least one sensor, the sensor data collected at a geographic location. The approach also involves extracting a set of input features from the sensor data, map data representing the geographic location, or combination thereof. The approach further involves processing the set of input features using a machine learning model to calculate a predicted sensor error of a target sensor operating at the geographic location. The machine learning model, for instance, has been trained on ground truth sensor error data to use the set of input features to calculate the predicted sensor error.