V2V Collaborative Positioning Using Relative Data
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Solution Overview
Problem
GPS-based positioning systems face accuracy issues due to natural and man-made obstructions, atmospheric delays, clock errors, and multi-path signals, often requiring more than the optimal number of satellites for accurate positioning, and existing vehicle-to-vehicle (V2V) collaboration methods struggle to enhance GPS data confidence when fewer satellites are available.
Innovation Solution
A vehicle-to-vehicle collaborative positioning system that utilizes GPS data from neighboring vehicles and relative positioning data from on-board sensing devices like radar, Lidar, or cameras to adjust and enhance the host vehicle's GPS data, with vehicles averaging each other's data if they have similar satellite signal quality within a defined accuracy radius, and broadcasting adjusted GPS data to increase confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a vehicle uses GPS positioning with fewer than optimal number of satellites, then positioning can still be obtained, but accuracy and confidence level deteriorate
Solution Approach 1:
The patent combines GPS data from multiple vehicles within a cluster to enhance the positioning accuracy of individual vehicles. By merging GPS observations from several vehicles, the system overcomes the limitation of having fewer than optimal satellite signals available to any single vehicle, thereby improving measurement precision while maintaining positioning availability.
Solution Approach 2:
The patent introduces relative positioning data from on-board sensing devices (radar, Lidar, cameras) as an intermediary to bridge the gap between vehicles. This intermediary data allows vehicles to adjust and enhance their GPS positions by comparing relative distances and orientations, effectively compensating for reduced GPS signal quality.
2Reliability
If V2V collaboration is used to enhance GPS data, then confidence level improves, but system complexity increases
Solution Approach 1:
The patent segments the positioning system into individual vehicle processors that independently calculate and broadcast their own GPS data and confidence levels. Each vehicle operates as an independent unit, processing its own positioning information and sharing it with the cluster. This segmentation reduces overall system complexity by distributing computational tasks rather than requiring a centralized complex processing system.
Solution Approach 2:
Each vehicle in the cluster performs self-positioning enhancement using its own sensors and received V2V data. The vehicles autonomously determine their GPS positions, calculate confidence levels based on satellite signal quality, and adjust their positions using relative data from other vehicles. This self-service approach eliminates the need for external control systems, reducing system complexity while improving reliability.
3Measurement precision
If multiple vehicles average their GPS data, then positioning accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent applies local quality by having each vehicle process and average GPS data only from nearby vehicles within its accuracy radius, rather than processing data from all vehicles in the cluster. This localized approach maintains high positioning accuracy for each vehicle while significantly reducing the total data processing burden, as each vehicle only needs to process data from a limited local neighborhood.
Data Source
AI summary
A method is provided of enhancing GPS data of a host vehicle within a cluster. V2V messages within the cluster are exchanged. A respective vehicle having a highest GPS trust factor is identified utilizing GPS data within the V2V messages. The GPS data of the host vehicle is adjusted as a function of GPS position of the identified vehicle and relative position data between the host vehicle and the identified vehicle.