Lane Network Graph Updates Using Probe Data and Bayesian Matching
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Solution Overview
Problem
Existing navigation systems lack accurate lane-level information necessary for self-driving vehicles and advanced driver assistance systems, as current methods struggle to efficiently update lane network models using probe data, particularly in areas with spatial variance and concentration on lane centers.
Innovation Solution
A lane network graph (LNG) model is generated and updated using time-ordered sequences of location data from probe vehicles, incorporating Bayesian updates and map matching techniques to add new lanes and refine node positions, headings, and uncertainty measures, linked to a localization layer for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probe data is used to update lane network models, then lane-level navigation accuracy is improved, but the complexity of data processing and model updating increases
Solution Approach 1:
The system automatically processes probe data and updates the lane network graph model without manual intervention. The model self-updates by integrating new drive data, matching locations to existing nodes, and adjusting lane representations based on accumulated probe information, eliminating the need for manual map updates.
Solution Approach 2:
The system continuously collects probe data from vehicles and uses it to refine and update the lane network graph model. The updated model then provides improved navigation guidance back to vehicles, creating a closed-loop feedback system where real-world driving data continuously improves the accuracy of the navigation infrastructure.
2Adaptability or versatility
If the lane network model is continuously updated with new drives, then the adaptability to changing road conditions is improved, but the computational resources and time required for updating increase
Solution Approach 1:
The system performs map matching by comparing probe locations against the existing lane network graph model before full integration. This preliminary matching step identifies which probe data points correspond to known lanes and intersections, allowing the system to efficiently determine what updates are actually needed rather than processing all data comprehensively.
Solution Approach 2:
The system updates only the portions of the lane network graph model that are affected by new probe data. Rather than reprocessing the entire road network, it selectively updates specific lanes, intersections, or road segments where probe data indicates changes or improvements, significantly reducing computational overhead.
3Manufacturing precision
If Bayesian updates are applied to refine node positions and headings, then the precision of lane network data is improved, but the computational complexity of each update operation increases
Solution Approach 1:
The system uses probabilistic uncertainty estimates that can be quickly calculated and discarded after each update cycle. Rather than maintaining complex, persistent models of every possible error source, it uses simplified uncertainty representations that are computationally inexpensive to calculate and update, enabling frequent refinements without excessive computational burden.
Data Source
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AI summary
A new drive includes a plurality of instances of location data that are received. The location data was captured by a probe apparatus onboard a vehicle as the vehicle traversed a lane network. The plurality of instances of location data are map matched using a lane level map matcher. The matched nodes (e.g., nodes of the LNG model representing the lane network to which an instance of location data was map matched) are identified and updated based on the new drive. Unmatched instances of location data (e.g., instances of location data that cannot be satisfactorily matched to a node of the LNG model) may be used to generate new nodes of the LNG model. A portion of the LNG model may be updated based on an update to a linked localization layer.