Road Feature Point Extraction for Precise Lane Transition Control
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately extracting and providing relevant road information, leading to inefficient vehicle control, as they often extract unnecessary data and fail to precisely identify critical road features like acceleration and deceleration lane start and end points.
Innovation Solution
A method and system for extracting road feature points by comparing current and next road attributes using map and navigation information, determining road types, and outputting specific feature points and their distances, allowing for precise vehicle control through targeted data extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general road information is extracted from map information, then the vehicle can obtain road data, but the extracted information is not targeted and includes useless data while missing critical features
Solution Approach 1:
The patent extracts only the critical road feature information (acceleration lane start/end points, deceleration lane start/end points) from the map data, rather than processing all general road information. This selective extraction eliminates useless data while ensuring critical features are captured for accurate vehicle control
Solution Approach 2:
The patent applies different processing qualities to different parts of the road data: critical feature points receive detailed extraction and processing, while non-critical road segments receive minimal or no processing. This local differentiation optimizes the balance between information completeness and processing complexity
2Reliability
If all road information is processed to ensure completeness, then no critical information is missed, but data analysis complexity and processing time increase significantly
Solution Approach 1:
The system extracts only the essential road feature parameters (start and end points of acceleration and deceleration lanes) from the complete road data, achieving high reliability for critical control decisions while dramatically reducing processing time by excluding non-essential information
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of road information (critical feature points) rather than the entire dataset. This partial processing maintains sufficient reliability for vehicle control while improving processing speed
3Measurement precision
If targeted road feature information is extracted, then vehicle control accuracy improves, but the extraction process becomes more complex
Solution Approach 1:
The patent applies specialized extraction algorithms only to specific critical features (acceleration/deceleration lane boundaries) where high precision is needed, while using simpler processing for other road segments. This local differentiation achieves high measurement precision for critical points without uniformly increasing system complexity
Data Source
AI summary
Disclosed are a road feature point extraction method and system. The road feature point extraction method comprises: acquiring map information for the current position of a vehicle, wherein the map information comprises the attributes of the current road and the attributes of the next road section; comparing the attributes of the current road with the attributes of the next road section to determine the road type of the next road section; and extracting, in conjunction with the road type of the next road section and the map information corresponding to the next road section, a road feature point representing a road scene on the next road section, and outputting the name of the extracted road feature point and information of the relative distance between the road feature point and the vehicle.