Pocket Lane Geometry Extraction for Precision Map Detection
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Solution Overview
Problem
Current precision map information lacks standardized information on pocket lanes, making it difficult for vehicles to accurately recognize and respond to the presence of pocket lanes, which can lead to delayed collision detection and increased driving burden.
Innovation Solution
A method and device that utilize precision map information to extract geometry information and determine the existence of pocket lanes by analyzing changes in lane geometry, allowing for quick identification of pocket lanes and their entry or exit points, even when information varies by map provider.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If precision map information is used to extract geometry information for detecting pocket lanes, then the accuracy of pocket lane detection is improved, but the complexity of information processing increases
Solution Approach 1:
The method segments the precision map information processing into distinct stages: extracting geometry information from the precision map, analyzing changes in geometry information, and determining pocket lane existence based on these changes. This segmentation simplifies the overall complex task by breaking it into manageable processing steps, each handling a specific aspect of the data.
Solution Approach 2:
The method extracts only the necessary geometry information from the comprehensive precision map data, focusing specifically on lane center coordinates, lane widths, and lane connection relationships. By extracting only the relevant geometric parameters needed for pocket lane detection, the system reduces processing complexity while maintaining detection accuracy.
2Adaptability or versatility
If various precision map information from different providers is analyzed, then the adaptability of the system is improved, but the difficulty of handling information deviations increases
Solution Approach 1:
The method employs a universal geometry information extraction framework that can process precision map data from multiple providers (Naver, Kakao, HERE, TomTom, Google). By designing the extraction process to focus on common geometric parameters (lane centers, widths, connections) that exist across different map providers, the system achieves multi-functionality and adaptability while systematically handling information deviations through standardized processing rules.
3Reliability
If geometry information is extracted and analyzed to determine pocket lane existence, then the reliability of collision detection is improved, but the loss of time for information processing increases
Solution Approach 1:
The method performs preliminary extraction of geometry information from the precision map before actual navigation or collision detection scenarios arise. By pre-processing and storing the essential geometric characteristics of lanes and their connections, the system prepares the data structure in advance, so that during real-time operation, only simple change detection and comparison operations are needed, significantly reducing processing time while maintaining high reliability.
Data Source
AI summary
According to the present disclosure, there may be provided a device and a method for extracting information including receiving precision map information, and extracting geometry information associated with each lane in a preset section using the precision map information, and determining the existence of a pocket lane using change information of the geometry information.