Vehicle-Sourced Detailed Map Data for Faster Reliable Road Updates
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Solution Overview
Problem
Current technologies face challenges in rapidly establishing reliable detailed map data for autonomous vehicles, especially in dynamic road conditions, due to limitations in perception sensors and the need for precise, high-frequency updates.
Innovation Solution
A method where a first vehicle collects and processes traveling data using sensors, determines the reliability of the data, and shares it with external devices or other vehicles, enabling the creation and dissemination of accurate, dynamic detailed map data for autonomous navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If detailed map data is collected from multiple vehicles traveling the same path, then the reliability and coverage of map data improves, but the time and complexity of data validation increases
Solution Approach 1:
The system performs preliminary actions by having vehicles continuously collect and pre-validate traveling data during normal operations. Data is stored and pre-processed in advance, so when map updates are needed, the validation work has already been partially completed, reducing the time required to establish reliable detailed map data.
Solution Approach 2:
The system implements feedback mechanisms where collected traveling data is continuously validated against existing map data, and discrepancies are fed back for correction. This iterative feedback process improves reliability over time while automating the validation to reduce manual time investment.
2Measurement precision
If detailed map data is collected with high precision in units of centimeters, then the accuracy of autonomous navigation improves, but the quantity of data and storage requirements increase
Solution Approach 1:
The system segments map data into different levels of detail and importance. High-precision data is collected only for critical elements (lane markings, traffic signals, obstacles) while less critical elements use lower precision. This segmentation maintains navigation accuracy for essential features while reducing overall data quantity.
Solution Approach 2:
The system applies local quality by varying the precision level根据不同 spatial locations and data types. Areas with high traffic density or complex intersections receive higher precision data collection, while open roads use lower precision. This approach optimizes the balance between accuracy and data volume.
3Adaptability or versatility
If map data is updated frequently to reflect dynamic road conditions, then the adaptability to changing environments improves, but the processing load and energy consumption increase
Solution Approach 1:
The system implements periodic action by updating map data at scheduled intervals rather than continuously. Vehicles collect data continuously but perform full map updates only at predetermined times or when triggered by significant changes. This periodic approach maintains adaptability while significantly reducing processing load and energy consumption compared to continuous updates.
Solution Approach 2:
The system uses dynamics by adjusting the update frequency based on real-time conditions. When road conditions are stable, updates occur less frequently to save energy. When significant changes are detected (accidents, construction, weather events), the system dynamically increases update frequency. This adaptive dynamic approach optimizes the balance between adaptability and energy consumption.
Data Source
AI summary
Provided is a method, performed by a first vehicle, of providing detailed map data, the method including collecting first traveling data about a first path using at least one first sensor while the first vehicle is traveling the first path; obtaining first detailed map data corresponding to the first path based on the first traveling data about the first path; and providing the first detailed map data to at least one external device.