Terrain-Based GNSS Augmentation for Lane-Level Vehicle Localization
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Solution Overview
Problem
Existing methods struggle to accurately determine the number of lanes and their relative positioning in multi-lane roads, especially when surface and subsurface characteristics vary significantly, leading to inaccuracies in vehicle localization and lane identification.
Innovation Solution
A method that involves collecting and clustering road profile data from multiple traverses using sensors and GNSS systems to identify similar profiles, determining representative lateral offsets, and calculating the number and ordering of lanes, with real-time corrections to GNSS signals based on terrain data to improve positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road profile data from multiple traverses is collected and clustered to determine lane ordering, then lane identification accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary clustering of road profile data during off-road conditions or when not actively navigating, pre-processing the terrain information before it is needed for lane identification. This allows the navigation system to quickly reference pre-clustered data during actual travel, reducing real-time processing requirements while maintaining high accuracy.
Solution Approach 2:
The system automatically collects road profile data during normal vehicle operation and performs clustering without requiring manual intervention. The navigation system self-updates its terrain database by processing new traverses and integrating them with existing clustered data, eliminating the need for separate data collection missions and reducing overall processing time.
2Measurement precision
If clustering is performed on all collected road profiles to determine lane characteristics, then lane ordering accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the road profile data processing by dividing the road into multiple sections and performing clustering on each segment independently. This allows parallel processing of different road segments, reducing overall computational complexity while maintaining accurate lane identification for each segment. The segmented approach also enables incremental updates as new traverse data becomes available.
Solution Approach 2:
The system dynamically adjusts clustering parameters such as similarity thresholds and data sampling rates based on road characteristics and available computational resources. For simple roads with clear lane distinctions, less computational effort is required, while complex multi-lane intersections receive more processing power. This adaptive parameter adjustment optimizes the balance between accuracy and computational complexity.
Data Source
AI summary
Methods and systems related to determining the ordering and/or positioning of travel lanes on a road segment are disclosed. In some embodiments, this may include obtaining and clustering road surface profiles associated with a road segment using, for example, a degree of similarity or other appropriate metric. A lateral offset or position of the clustered profiles may be used in determining the lane ordering and/or position. The resulting lane specific information may be used to determine a travel lane for a vehicle by comparing a current road-profile obtained from the vehicle and the road profile information associated with the different lanes. In other embodiments, a method and/or system for augmenting a global navigation satellite system (GNSS) signal may include using a raw GNSS signal and a GNSS location associated with terrain-based data to determine a lateral offset for use in determining a corrected GNSS location of the vehicle.