GNSS Error Map Weighting for Vehicle Sensor Fusion Positioning
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Solution Overview
Problem
Existing vehicle navigation systems face discrepancies and varying accuracy issues due to conflicting sensor data and location-dependent GNSS errors, particularly in environments with signal blockage and multipath interference, which current systems fail to adequately address.
Innovation Solution
A system that determines GNSS position accuracy and assigns weights based on a variable error model, using a GNSS error map divided into spatial regions, accounting for factors like signal blockage and multipath errors, and incorporating relative vehicle positions from vehicle sensors and local maps through interpolation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed weighting system is used for sensor fusion, then the system is simple to implement, but it cannot adapt to varying GNSS accuracy in different locations
Solution Approach 1:
The system performs preliminary actions by pre-collecting GNSS position data and sensor data at multiple locations, pre-calculating discrepancy values, and pre-generating an error model map before actual vehicle operation. This allows the system to adapt to varying GNSS accuracy without adding complexity during real-time operation, as the adaptive weighting is already prepared in advance
Solution Approach 2:
The error model map is segmented into multiple spatial regions, each with its own characteristic discrepancy values and weighting factors. This segmentation allows the system to handle different locations with different GNSS accuracy characteristics independently, improving adaptability while keeping the complexity manageable through localized processing
2Measurement precision
If uniform error weighting is applied to all locations, then the system is easy to operate, but it produces inaccurate positioning in areas with signal blockage or multipath interference
Solution Approach 1:
The system applies local quality by assigning different error weighting values to different spatial regions based on their specific characteristics. Each location receives customized weighting factors calculated from locally collected discrepancy data, ensuring high positioning accuracy in each specific area while the overall system remains automated and easy to operate
3Measurement precision
If location-specific error modeling is implemented, then positioning accuracy improves, but the system requires extensive data collection and processing
Solution Approach 1:
The system performs preliminary data collection and error model generation before actual vehicle operation. By collecting GNSS and sensor data in advance at multiple locations and pre-calculating discrepancy values, the system prepares adaptive weighting factors ahead of time, reducing real-time processing requirements and minimizing time loss during operation
Solution Approach 2:
The system merges data from multiple vehicles to collectively build the error model map. By combining observations from multiple vehicles operating in the same area, the system accelerates data collection and achieves statistically significant discrepancy values faster, reducing the time required for comprehensive error model generation
Data Source
AI summary
A vehicle includes a controller having a global navigation system satellite (GNSS) positioning module and a sensor fusion module. A plurality of vehicle sensors are connected to the controller. The sensor fusion module includes software configured to fuse sensor data from the plurality of vehicle sensors and a GNSS position by applying an error weight to each element of data from the plurality of vehicle sensors and the GNSS position. The error weight of the GNSS position is variable dependent upon a GNSS error model map.