Connected Vehicle Map Update Bandwidth Optimization
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Solution Overview
Problem
Current map update systems face challenges in efficiently transferring and managing data between user devices and map databases, leading to increased costs and reduced system performance due to redundant data transfers and the need for frequent updates in dynamic geographic environments.
Innovation Solution
A method involving connected vehicles equipped with communication devices and environment sensor arrays that detect road furniture items, which delay data transmission to a server until updates are confirmed and combine sensor data to reduce redundant updates, using confidence levels to determine the necessity of data transfer, thereby minimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map update systems transfer data frequently from user devices to map databases, then map accuracy is improved, but bandwidth consumption and system costs increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and validating map update data locally at the map database before transmission. It anticipates which updates are necessary by checking confidence levels and comparing against existing map data, thereby preventing unnecessary bandwidth consumption while ensuring accurate map updates when needed.
Solution Approach 2:
The system implements feedback mechanisms where the map database receives confidence levels from user devices and uses this feedback to determine whether to process and apply updates. This feedback loop allows the system to maintain map accuracy by only processing confirmed updates, reducing unnecessary bandwidth usage.
2Quantity of substance
If the system processes all sensor data from connected vehicles, then map update completeness is improved, but data processing time and system complexity increase
Solution Approach 1:
The system extracts and processes only the necessary portions of sensor data by filtering out redundant information. It identifies and processes only those map attributes that have changed and have sufficient confidence levels, thereby reducing processing complexity while maintaining update completeness for critical map elements.
Solution Approach 2:
The system applies local quality by treating different map attributes and update confidence levels differently. High-confidence updates to critical map elements are processed immediately, while low-confidence or redundant updates are filtered out, reducing overall processing complexity while maintaining necessary completeness.
3Measurement precision
If the system transfers comprehensive map update data to all connected vehicles, then map accuracy is improved, but network load and transmission costs increase
Solution Approach 1:
The system segments map update data into essential and non-essential components. It transmits only the necessary update information to connected vehicles, segmenting the data flow to reduce network load while maintaining map accuracy for critical elements. This selective transmission improves system efficiency by avoiding unnecessary data transfers.
Data Source
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AI summary
A road furniture item or another type of road object is detected by at least one sensor. An associated geographic position associated with the road furniture item or road object is determined. After a predetermined time is reached, the geographic position is compared to a local database. A remote database is updated after the predetermined time is reached and according to the comparison with the local database. In some examples, the data indicative of the road furniture item or the road object is deleted based on a confidence level in the local database.