Lane-Level Intersection Mapping via Grouped Probe Data
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Solution Overview
Problem
Traditional road maps provide limited information about lanes at road intersections, making it difficult to design mathematical algorithms for accurate lane-level modeling, which is crucial for advanced navigation and Highly Automated Driving applications.
Innovation Solution
An apparatus and method that generate and provide grouped probe data using vehicle trajectories with common heading angles at road intersections, allowing for lane-level mapping by clustering and filtering data to define paths of travel and number of lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional road maps are used, then the device complexity is low, but the measurement precision of lane-level information is insufficient
Solution Approach 1:
The system uses probe data from vehicles themselves (GPS coordinates, heading angles) to automatically generate lane-level mapping information. The vehicles serve as both the data collection instruments and the subjects being mapped, eliminating the need for external surveying equipment or manual mapping efforts.
Solution Approach 2:
The patent replaces traditional mechanical surveying methods with computational processing of digital probe data. Instead of physical measurement tools, the system uses algorithms to process GPS coordinates and heading angles, applying mathematical clustering techniques to automatically identify lane structures and intersection geometries.
2Measurement precision
If probe data from all vehicles is collected, then the quantity of data increases improving mapping accuracy, but the device complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential features from probe data—specifically GPS coordinates and heading angles—at critical points (entry and exit of intersections). By focusing on these key parameters rather than processing complete trajectory data, the system reduces computational complexity while maintaining mapping accuracy.
Solution Approach 2:
The patent segments the probe data collection into discrete, manageable components: entry points, exit points, and heading angles at each intersection. This segmentation allows the system to process data in structured units that can be efficiently clustered and analyzed, rather than handling continuous raw trajectory data.
3Loss of information
If detailed lane-level information is provided, then the loss of information is reduced, but the ease of operation becomes more complex
Solution Approach 1:
The system performs preliminary processing of probe data to pre-identify lane structures, intersection geometries, and valid trajectory patterns before navigation use. By pre-processing and structuring the data during the mapping phase, the actual navigation operation becomes simpler, as users receive ready-to-use lane guidance without needing to interpret raw data.
Data Source
AI summary
An apparatus comprising a processor and memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus at least to:generate, in respect of a road intersection, grouped probe data using probe data derived from probed vehicular movements through the road intersection, wherein the grouped probe data is generated by grouping together probe data comprising vehicle trajectories which have respective common heading angles at points of entry to and exit from the road intersection; andprovide the grouped probe data for use in lane-level mapping of the road intersection.