Intersection Map Representation Using Lane Conflict Clusters
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Solution Overview
Problem
The process of constructing road network maps for autonomous driving is highly manual and requires human intervention, particularly for identifying lane boundaries and intersections, which is challenging due to the variability of intersection data objects that do not lend themselves to policy guidelines.
Innovation Solution
An automated system that identifies conflicting lane segments, generates conflict clusters representative of intersections, and creates data representations including outer and inner geometric boundaries, allowing for the creation of intersection data objects that encode parameters like inlets, outlets, and yielding relationships within the road network map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual human intervention is used to identify lane boundaries and intersections, then high-quality consistent maps can be produced, but the process is highly manual and time-consuming
Solution Approach 1:
The system enables autonomous vehicles to automatically generate and contribute intersection data objects while navigating, eliminating the need for manual human intervention in map construction. The vehicle's own navigation data and sensor information are used to create and update map elements autonomously.
Solution Approach 2:
The system implements a feedback mechanism where intersection data objects generated by autonomous vehicles are validated, processed, and used to update the road network map, which in turn improves future navigation and data generation. This closed-loop system continuously improves map quality through automated feedback from real-world operation.
2Manufacturing precision
If policy guidelines are applied to lane identification, then consistent labeling can be achieved, but intersection data objects are widely varied and do not lend themselves to policy guidelines
Solution Approach 1:
The system segments the complex intersection environment into distinct data objects including inlet lane segments, outlet lane segments, conflicted space polygons, and metadata. This segmentation allows each element to be processed and validated independently according to specific guidelines while capturing the overall variability of intersections.
Solution Approach 2:
The system uses parameter-based definitions for intersection data objects, where geometric properties, lane attributes, and spatial relationships are defined through configurable parameters. This allows the same data structure to adapt to various intersection types and configurations while maintaining consistency through parameter validation.
3Productivity
If automated systems are used to generate intersection data, then productivity increases, but the complexity of identifying conflicting lane segments and generating geometric boundaries increases
Solution Approach 1:
The system performs preliminary identification of conflicting lane segments by analyzing lane segment relationships, geometric overlaps, and traffic flow patterns before generating the complete intersection data object. This preliminary processing simplifies the subsequent steps of creating geometric boundaries and validating the intersection structure.
Data Source
AI summary
A system receives a road network map that corresponds to a road network that is in an environment of an autonomous vehicle. For each of the one or more lane segments, the system identifies one or more conflicting lane segments from the plurality of lane segments, each of which conflicts with the lane segment, and adds conflict data pertaining to a conflict between the lane segment and the one or more conflicting lane segments to a set of conflict data. The system analyzes the conflict data to identify a conflict cluster that is representative of an intersection. The system groups predecessor lane segments and the successor lane segments as inlets or outlets of the intersection, generates an outer geometric boundary of the intersection, generates an inner geometric boundary of the intersection, creates a data representation of the intersection and adds the data representation to the road network map.