Transformer Lane Graph Refinement for Scalable HD Road Mapping
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Solution Overview
Problem
Current approaches to creating high-definition lane graphs for autonomous vehicles are not scalable, requiring significant resources and struggling with producing accurate geometries and connectivity, especially for large areas.
Innovation Solution
A method using a transformer-based road mapping framework that executes an extraction model to generate lane features, a coarse model to generate coarse boundary embeddings and a coarse lane graph, and a refinement model to update the prior lane graph with a refined lane graph, achieving precise and topologically correct lane graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If automated methods are used to generate lane graphs, then resource consumption is reduced, but accuracy and connectivity of lane geometries deteriorate
Solution Approach 1:
The automated lane graph generation process is divided into multiple processing stages: initial extraction model for basic lane detection, refinement model for improving geometric accuracy, and connectivity model for establishing topological relationships. This segmentation allows each stage to focus on specific aspects of lane graph creation, achieving high precision without requiring extensive manual annotation resources.
Solution Approach 2:
The system performs preliminary actions by pre-processing satellite imagery to extract road network structures before detailed lane graph generation. The extraction model initially identifies road boundaries and lane markings, creating a foundation that subsequent refinement models build upon, thereby reducing the computational resources needed for final high-precision generation.
2Productivity
If automated methods are used to generate lane graphs, then productivity increases, but connectivity and global coherence deteriorate
Solution Approach 1:
The system implements feedback mechanisms where the connectivity model continuously evaluates the topological relationships between detected lane segments and adjusts the generation process accordingly. The model receives feedback about connectivity errors and iteratively refines the lane graph to ensure global coherence, maintaining reliability while preserving high productivity through automated processing.
Solution Approach 2:
The patent combines multiple models (extraction model, refinement model, and connectivity model) into an integrated automated generation system. These models work together in a unified framework where the extraction model detects initial lane structures, the refinement model improves geometric accuracy, and the connectivity model ensures topological correctness, achieving both high productivity and reliable connectivity.
3Manufacturing precision
If manual annotation is used to create lane graphs, then accuracy is improved, but resource consumption and time increase
Solution Approach 1:
The system performs preliminary automated extraction of lane features from satellite imagery before final graph generation. The extraction model pre-identifies road boundaries, lane markings, and intersection structures, creating a prepared dataset that reduces the time required for subsequent refinement and connectivity establishment, achieving high precision without manual annotation time.
Solution Approach 2:
The automated generation system performs self-service by using the extraction model to initially detect lane structures and then using the refinement and connectivity models to automatically improve accuracy and ensure proper connectivity. This self-service automated process eliminates the need for time-consuming manual annotation while maintaining high precision through iterative refinement.
4Loss of energy
If existing automated systems are used, then resource consumption is reduced, but inference time increases
Solution Approach 1:
The processing pipeline is segmented into distinct models that operate in sequence: extraction model for initial detection, refinement model for accuracy improvement, and connectivity model for topological validation. This segmentation allows efficient resource allocation where simpler models handle basic tasks quickly, reducing overall inference time while maintaining low computational resource usage through optimized processing at each stage.
Data Source
AI summary
A method implements a road mapping framework. The method includes executing an extraction model to generate multiple lane features from a lane image. The method further includes executing a coarse model to generate multiple coarse boundary embeddings and a coarse lane graph from the lane features and multiple prior boundary embeddings using a transformer decoder. The prior boundary embeddings is generated from a prior lane graph. The method further includes executing a refinement model to update the prior lane graph with a refined lane graph to form an updated lane graph. The refined lane graph is generated from multiple refined boundary embeddings that is output from a transformer encoder. The transformer encoder generates the refined boundary embeddings from the coarse boundary embeddings combined with multiple point embeddings corresponding to the coarse boundary embeddings.