GNSS Deviation Map Layers for Vehicle Position Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
GNSS data accuracy varies significantly and existing measures do not fully account for all factors affecting reliability, leading to potential inaccuracies in vehicle navigation and operation.
Innovation Solution
A GNSS deviation map layer is created using positional deviations from vehicles, combining sensor and map data to assess GNSS reliability, which updates in real-time and utilizes machine learning to refine classifications based on positional deviations and covariance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing GNSS reliability measures are used, then the system is simple to operate, but the measurement precision of GNSS data accuracy is insufficient
Solution Approach 1:
The patent introduces map data as an intermediary reference system to assess GNSS reliability. By comparing GNSS-derived positions with map-based expected positions, the system obtains an independent verification of GNSS accuracy without directly modifying the GNSS receiver itself. This mediator approach enables precise reliability measurement while keeping the core GNSS system simple.
Solution Approach 2:
The system creates a virtual copy of the vehicle's position through map data and sensor fusion, then compares this copied position with the GNSS-reported position. This copying mechanism allows reliability assessment by contrasting two independent position determination methods, achieving high measurement precision without complicating the actual vehicle positioning hardware.
2Reliability
If a comprehensive reliability assessment system is implemented, then the reliability of GNSS data improves, but the device complexity increases
Solution Approach 1:
The patent makes existing map data and sensor systems serve multiple functions: they continue to provide navigation and environmental information while simultaneously enabling GNSS reliability assessment. This multi-functionality approach increases reliability without proportionally increasing system complexity, as the same hardware infrastructure supports both primary navigation and verification functions.
Solution Approach 2:
The system implements feedback by continuously comparing GNSS positions with map-based expected positions and using the deviations to update reliability assessments. This feedback loop automatically adjusts the reliability classification based on observed performance, improving GNSS data reliability through continuous verification without requiring complex manual intervention or system reconfiguration.
3Reliability
If real-time updates of deviation map layer are performed, then the reliability assessment remains current and accurate, but the processing time and computational resources increase
Solution Approach 1:
The system performs partial updates of the deviation map layer, focusing computational resources on geographic areas where vehicles are currently operating or where reliability changes are most likely to occur. Rather than continuously recalculating the entire map layer, the system updates only relevant portions, maintaining real-time reliability assessment while reducing overall processing time and computational burden.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive a positional deviation for a vehicle and update a classification of a geographic area in a GNSS deviation map layer based on the positional deviation. The positional deviation is based on sensor data generated by environmental sensors on board the vehicle, map data, and global navigation satellite system (GNSS) data received at the vehicle. The positional deviation indicates a difference between a GNSS pose of the vehicle derived from the GNSS data and a localized position of the vehicle indicated by the sensor data and the map data. The GNSS deviation map layer indicates a reliability of the GNSS data.