Automated Road Intersection Detection Using Sensor Mesh Triangulation
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Solution Overview
Problem
Creating highly detailed and accurate digital map data of road intersections for autonomous driving is time-consuming and prone to errors due to the manual fusion of traditional map data and sensor data, which does not scale well for large areas, leading to incomplete or inconsistent representations.
Innovation Solution
An automated method using mesh triangulation, specifically Delaunay triangulation, to generate a triangular mesh representation of road intersections based on sensor data, allowing for accurate detection and characterization of intersection features such as boundaries, lanes, and entry/exit roads, which can be integrated into geographic databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual fusion of traditional map data and sensor data is used, then detailed map data can be created, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of map data fusion with an automated computational system. Sensors mounted on vehicles automatically collect geographic data, and a processing system automatically fuses this data with existing map data using algorithms, eliminating the need for manual data integration while maintaining high accuracy standards.
Solution Approach 2:
The system enables self-service map updating by automatically collecting data from vehicles in service, processing the data through automated pipelines, and updating maps without requiring dedicated manual intervention. The vehicles themselves serve as mobile data collection stations, and the system self-corrects and self-updates map data continuously.
2Manufacturing precision
If manual methods are used for map data fusion, then detailed intersection representations can be achieved, but the method does not scale well for large areas
Solution Approach 1:
The patent replaces manual map updating methods with an automated data processing system that uses algorithms to fuse sensor data from multiple vehicles with existing map data. This computational approach maintains detailed intersection representations while enabling scalable map updates across large geographic areas simultaneously.
Solution Approach 2:
The system creates a universal automated pipeline that handles diverse data sources (multiple vehicles, different sensor types) and produces consistent high-quality map updates across all geographic areas. The same processing algorithms are applied universally to maintain detail quality whether updating one intersection or thousands across a large region.
3Measurement precision
If automated sensor-based detection is used, then map data accuracy can be improved, but the complexity of processing sensor data increases
Solution Approach 1:
The patent segments the complex sensor data processing task into distinct modular components: data collection from multiple sensors, data filtering and validation, coordinate transformation, fusion with existing map data, and quality assurance. This modular segmentation reduces processing complexity while maintaining high detection accuracy through systematic handling of each processing stage.
Solution Approach 2:
The system introduces intermediary processing layers between raw sensor data and final map outputs, including data validation filters, coordinate transformation intermediaries, and confidence scoring mechanisms. These intermediaries simplify the overall processing complexity by breaking down complex transformations into manageable steps with clear intermediate results.
Data Source
AI summary
An approach is provided for automated detection and/or characterization of road intersections. The approach, for instance, includes determining a set of observables associated with a road intersection. The set of observables comprises a plurality of point observations of road boundaries of the road intersection, and the plurality of point observations are collected using sensors of vehicles traveling in the road intersection. The approach also involves processing the plurality of point observations to generate a triangular mesh to represent a surface of the road intersection. The triangular mesh comprises a plurality of triangles connecting the plurality of point observations as respective vertices of a plurality of triangles. The approach further involves selecting one or more branch triangles of the plurality of triangles. The vertices of the branch triangles reference three different road boundaries of the road intersection. The approach further involves automatically detecting the intersection based on the branch triangles.