Road Feature Detection for Accurate Lane Network Map Updates
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to accurately update lane networks in high-precision maps due to changes in road conditions, such as construction, which can affect the connection relationships between lanes.
Innovation Solution
An apparatus and method that utilize a processor to detect features like signposts, road markings, lane lines, and stop lines from vehicle situation data to update the lane network, ensuring it matches the actual connection relationships between lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the lane network is updated frequently to reflect road condition changes, then the accuracy of autonomous driving control is improved, but the computational resources and time required for map updates increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing road feature data from multiple vehicles in advance. When map updates are needed, the processor can quickly retrieve pre-processed feature data instead of collecting and processing data from scratch, significantly reducing update time while maintaining accuracy.
Solution Approach 2:
The system enables self-service by allowing vehicles to automatically contribute their observed road features to the map update process without requiring manual intervention. The processor automatically processes incoming feature data, detects changes in lane connections, and updates the lane network autonomously, reducing both time and resource requirements.
2Reliability
If comprehensive road features are collected from multiple vehicles to update the lane network, then the reliability of map data is improved, but the complexity of data processing and integration increases
Solution Approach 1:
The system extracts only the essential road features (lane lines, signposts, road markings, stop lines) needed for lane network updates from the comprehensive data collected by multiple vehicles. The processor filters out redundant information and focuses on processing only the critical features that affect lane connections, reducing data processing complexity while maintaining reliability.
Solution Approach 2:
The processor acts as an intermediary that receives, standardizes, and integrates feature data from multiple vehicles with different sensors and data formats. It converts diverse input data into a unified representation that can be reliably processed for lane network updates, simplifying the integration complexity.
3Loss of information
If the system detects and processes multiple types of road features (signposts, road markings, lane lines, stop lines), then the completeness of lane connection information is improved, but the difficulty of detecting and measuring features increases
Solution Approach 1:
The system employs a universal detection approach where the processor can identify and process multiple types of road features (signposts, road markings, lane lines, stop lines) using the same underlying detection framework. This multi-functional capability allows comprehensive lane connection information to be obtained without proportionally increasing detection difficulty, as the system is designed to handle diverse feature types through unified processing methods.
Data Source
AI summary
An apparatus for updating a map detects a feature in an area around a vehicle traveling on a road from situation data representing the situation around the vehicle, and updates a lane network representing a connection relationship between lanes included in road sections into which the road is divided. The lane network is stored in a storage unit and updated to match a connection relationship between lanes indicated by the detected feature.