Road Situation Data Fusion With Redundancy Removal for Real-Time Queries
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Solution Overview
Problem
Existing systems fail to precisely recognize road situations using data from various road infrastructure sensors and remove unnecessary data, leading to inefficiencies in processing speed and lack of real-time context awareness for vehicles.
Innovation Solution
A method and apparatus that utilize a sensor group comprising Lidar, camera, and radar to collect data, model object relationships on a graph, construct a grid-based spatial index, and remove redundant data to provide real-time context awareness for vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from various road infrastructure sensors is collected and processed, then road situation recognition precision is improved, but processing time increases
Solution Approach 1:
The patent extracts and removes redundant data from the collected sensor data. The data processing apparatus identifies and eliminates duplicate or unnecessary information while preserving essential road situation data, thereby reducing processing time without compromising recognition precision
Solution Approach 2:
The patent segments the data processing into distinct stages: data collection from multiple sensors, redundancy identification, redundant data removal, and final road situation recognition. This segmented approach allows for optimized processing at each stage, improving overall efficiency
2Measurement precision
If data from various road infrastructure sensors is collected, then road situation recognition precision is improved, but processing speed decreases
Solution Approach 1:
The patent extracts and removes redundant data from the collected sensor data. The data processing apparatus identifies and eliminates duplicate or unnecessary information while preserving essential road situation data, thereby reducing processing time without compromising recognition precision
Solution Approach 2:
The patent discards redundant data that does not contribute to road situation recognition while recovering and preserving only the essential information needed for accurate analysis, thus improving processing speed
3Quantity of substance
If redundant data is not removed, then data completeness is maintained, but processing efficiency decreases
Solution Approach 1:
The patent extracts and removes redundant data from the collected sensor data. The data processing apparatus identifies and eliminates duplicate or unnecessary information while preserving essential road situation data, thereby reducing processing time without compromising recognition precision
Solution Approach 2:
The patent applies partial action by selectively processing only the essential portions of the data while identifying and removing redundant portions, achieving optimal balance between data completeness and processing efficiency
Data Source
Figure 1
Figure 2~3A
Figure 3B
AI summary
The present disclosure relates to an apparatus and a method for processing road situation data. A road situation data processing method according to an exemplary embodiment of the present disclosure is a road situation data processing method which is performed by a processor of an apparatus for processing road situation data including: collecting sensing data on objects on a road from a plurality of sensors provided on the road; modeling a relationship between the objects on a graph based on sensing data on the objects; constructing a grid-based spatial index with respect to the graph modeling result; removing redundant sensing data among sensing data on the objects included in the grid-based spatial index; extracting an object corresponding to a response to a query by performing a predetermined query on the objects from which the redundant sensing data is removed; and outputting context awareness data to the object corresponding to the response to the query.