Vehicle Positioning Error Range Integrity Monitoring
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Solution Overview
Problem
Current positioning systems for intelligent driving vehicles rely on multiple sensors, but existing integrity monitoring methods using similarity indices cannot effectively meet the performance requirements, such as lane-level positioning accuracy, leading to inefficiencies and complexity in data fusion and reliability testing.
Innovation Solution
The method involves each positioning subsystem determining its own results and error ranges, with a fusing module selecting reliable results for fusion, calculating an error range for the positioning information, and using this error range to characterize integrity, ensuring the positioning information meets performance requirements while simplifying calculations and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used for positioning, then positioning reliability is improved, but system complexity increases
Solution Approach 1:
The positioning system is divided into multiple independent positioning subsystems (GNSS, inertial sensor, visual positioning, etc.), each capable of independently providing positioning results. This segmentation allows the system to maintain high reliability through diversity while managing complexity by treating each subsystem as an independent module with its own error range calculation and reliability assessment.
Solution Approach 2:
A fusing module is introduced as an intermediary component that receives positioning results and error ranges from multiple subsystems, performs reliability detection, and fuses the results. This intermediary handles the complexity of multi-sensor integration centrally, simplifying the overall system architecture while maintaining reliability through systematic error range-based fusion.
2Reliability
If similarity index is used for integrity monitoring, then monitoring capability is provided, but adaptability to performance requirements deteriorates
Solution Approach 1:
The integrity monitoring approach is changed from using a dimensionless similarity index to using error ranges that have direct physical meaning and can be compared with performance requirements. By changing the parameter from similarity (0-1 range) to error range (distance units), the system gains adaptability to specific performance requirements such as lane-level positioning accuracy while maintaining integrity monitoring capability.
3Measurement precision
If multiple sensors are fused, then positioning accuracy is improved, but calculation complexity increases
Solution Approach 1:
Each positioning subsystem pre-calculates its own error range before fusion, and the fusing module performs reliability detection based on these pre-calculated error ranges. This preliminary action eliminates the need for complex real-time covariance propagation and uncertainty quantification during fusion, significantly reducing calculation complexity while maintaining positioning accuracy through systematic error range-based selection and fusion.
Data Source
AI summary
The present disclosure provides a method and an apparatus for determining positioning information of a vehicle, an electronic device, a storage medium and a computer program product, relating to the field of artificial intelligence, in particular to the field of intelligent driving. The method includes: receiving positioning results and error ranges of the positioning results from at least two positioning subsystems in a vehicle; determining reliability detection results of the positioning results of the positioning subsystems according to the error ranges of the positioning results; and determining positioning information of the vehicle according to a first positioning result of a positioning subsystem whose reliability detection result is “reliable”, and determining an error range of the positioning information, where the error range of the positioning information represents a confidence level of the positioning information.