Lane-Level Map Matching Using V2V Relative Positioning
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Solution Overview
Problem
Current GPS technologies face challenges in achieving lane-level accuracy, especially in urban areas with tall buildings and complex road geometries, which is crucial for automated vehicle systems requiring precise positioning for safety and mobility applications.
Innovation Solution
A system and method that utilizes Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to enhance GPS accuracy, incorporating algorithms for lane-level position estimation, confidence assessment, and self-correction, leveraging relative GPS accuracy between host and remote vehicles to determine lane position and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS technology is used for vehicle positioning, then positioning coverage is wide and operation is simple, but positioning accuracy deteriorates in urban areas with tall buildings and complex road geometries
Solution Approach 1:
The patent introduces map data and V2V communication data as intermediary elements to bridge the gap between GPS signals and accurate lane-level positioning. Map matching algorithms use road geometry data as a mediator to correct GPS positions, while V2V communication mediates by providing relative position information from other vehicles to disambiguate positioning in complex urban environments.
Solution Approach 2:
The patent combines multiple positioning data sources (GPS, map matching, V2V communication) into a unified positioning system. By merging these different information sources, the system achieves lane-level accuracy that exceeds the capabilities of any single source, particularly in urban areas where GPS alone is insufficient.
2Measurement precision
If map matching algorithms are used to improve positioning accuracy, then lane-level accuracy is achieved, but system complexity increases due to multiple data processing requirements
Solution Approach 1:
The patent creates a multi-functional positioning system that performs multiple tasks simultaneously: GPS positioning, map matching, V2V data processing, and confidence assessment all within a single integrated framework. This universal system handles diverse data types and processing requirements without requiring separate dedicated systems for each function.
Solution Approach 2:
The system includes self-correction capabilities where the positioning algorithm automatically adjusts for errors using map geometry constraints and V2V relative position information. The confidence assessment mechanism self-evaluates positioning quality and can trigger re-positioning or alternative methods when accuracy is insufficient, reducing the need for external intervention.
3Measurement precision
If V2V communication is used to enhance positioning, then lane position determination is improved, but communication infrastructure requirements and system complexity increase
Solution Approach 1:
The patent implements V2V communication as an optional enhancement rather than a mandatory requirement. The system can operate with partial V2V data from some vehicles or with no V2V data at all, falling back to map matching and GPS. This partial action approach provides positioning improvement where V2V data is available without making the system dependent on universal V2V infrastructure coverage.
Data Source
AI summary
In one example, we describe a method, system, and infrastructure for dealing with lane-level matching problem, and provide different methods of estimating position corrections, position and map matched confidence, self-correcting map matching (i.e., Map Matching Algorithm (at lane-level)), and turning and lane change events. On the top of the described algorithms, the V2V data, when available, can be used to help the entire discussed algorithm steps. If the relative GPS accuracy between the host vehicle and the remote vehicles in the proximity of the host vehicle is enough to separate vehicle in lane, and there are enough vehicles to exist in all lanes of interest, then it becomes an easier job to determine which lane the host vehicle is in, and this can reflect very positively on all the above algorithm steps.