Lane Graph Estimation via Maximum Likelihood Cycle Validation
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Solution Overview
Problem
Current methods for generating and estimating lane graphs from frame graphs are labor-intensive, time-consuming, and resource-intensive, often resulting in inaccuracies due to the incorporation of outliers and require significant manual labor for image detection and labeling.
Innovation Solution
A system and method that uses a maximum likelihood estimation process to receive a frame graph of a road portion, identify inconsistencies, and resolve them by removing or reassigning edges with invalid relationship options, thereby estimating a lane graph with improved accuracy and reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to generate semantic road maps and lane graphs, then detailed road information can be obtained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computational system. The processor automatically receives frame graphs, identifies cycles, detects invalid edges, and generates lane graphs through algorithmic processing rather than human manual work, thereby eliminating the trade-off between accuracy and time consumption.
Solution Approach 2:
The system performs self-correction by automatically identifying and removing invalid edges through cycle detection and probability analysis. The algorithm autonomously validates its own output by checking for inconsistencies in the frame graph structure, eliminating the need for manual verification and revision.
2Reliability
If traditional methods are used to process frame graphs, then comprehensive road data can be captured, but computational resources and storage requirements increase significantly
Solution Approach 1:
The patent extracts and removes invalid edges from the frame graph through cycle detection and probability threshold filtering. By identifying edges that create inconsistent cycles and removing those with low probability values, the system eliminates unnecessary data processing and storage requirements while maintaining graph reliability.
Solution Approach 2:
The system applies partial processing by focusing only on critical validation steps (cycle detection and probability filtering) rather than processing all possible edge combinations. This selective approach reduces computational complexity from exponential to polynomial time while maintaining adequate reliability.
3Measurement precision
If manual image detection and labeling are performed, then accurate road features can be identified, but the process requires significant manual labor
Solution Approach 1:
The patent replaces manual image detection and labeling with automated computer vision algorithms. The system processes frame graphs and trace points through algorithmic edge detection and relationship probability calculation, automatically identifying road features without human intervention while maintaining detection accuracy.
Solution Approach 2:
The system creates a simplified computational representation (frame graph) of the complex visual road scene. By copying essential geometric and topological relationships into a graph structure with trace points and edges, the system enables automated processing while preserving the critical information needed for accurate lane graph generation.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to estimating a lane graph describing a road portion. In one embodiment, a method includes receiving a frame graph of a road portion. The frame graph has a plurality of cycles, and each cycle is an enclosed configuration of three or more edges. Each edge connects two trace points. Further, each edge has one or more relationship options between the two trace points and a probability value for each of the one or more relationship options. Each trace point is related to a position of a vehicle in the road portion. The method includes estimating, using a maximum likelihood estimation (MLE) process, a lane graph describing the road portion based on at least a portion of the frame graph.