Map Update Section Selection for Autonomous Driving
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Solution Overview
Problem
Current methods for determining target road sections for updating map information in autonomous driving systems are inefficient, as they do not effectively prioritize areas where generating or updating map data will provide the greatest advantage for autonomous driving control within limited resources.
Innovation Solution
An apparatus and method that select road sections for map updates by identifying areas where map information is unavailable, calculating a score for the improvement in drivers' convenience based on the difference between manual and autonomous driving costs, and prioritizing these sections for data collection to maximize the benefit of autonomous driving control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If map information is updated for all road sections, then autonomous driving availability is improved, but communication costs and man-hours increase
Solution Approach 1:
The patent applies local quality by selectively updating map information only in specific road sections where it provides the greatest benefit. Instead of uniform updates across all roads, the system identifies and prioritizes particular sections based on criteria such as autonomous driving availability, traffic volume, and driver convenience improvement, thereby optimizing resource allocation.
Solution Approach 2:
The patent changes the parameter of map update priority by introducing a scoring mechanism that evaluates road sections based on multiple factors including autonomous driving availability, traffic volume, and driver convenience. This parameter transformation allows the system to dynamically prioritize which sections deserve update resources.
2Reliability
If map information is updated for all road sections, then autonomous driving availability is improved, but man-hours increase
Solution Approach 1:
The patent applies local quality by selectively updating map information only in specific road sections where it provides the greatest benefit. Instead of uniform updates across all roads, the system identifies and prioritizes particular sections based on criteria such as autonomous driving availability, traffic volume, and driver convenience improvement, thereby optimizing resource allocation.
Solution Approach 2:
The patent changes the parameter of map update priority by introducing a scoring mechanism that evaluates road sections based on multiple factors including autonomous driving availability, traffic volume, and driver convenience. This parameter transformation allows the system to dynamically prioritize which sections deserve update resources.
3Loss of information
If data collection is performed on all road sections, then map information completeness is improved, but communication cost increases
Solution Approach 1:
The patent applies local quality by selectively updating map information only in specific road sections where it provides the greatest benefit. Instead of uniform updates across all roads, the system identifies and prioritizes particular sections based on criteria such as autonomous driving availability, traffic volume, and driver convenience improvement, thereby optimizing resource allocation.
Solution Approach 2:
The patent applies partial action by collecting data only from road sections that meet specific priority criteria rather than all sections. The system performs partial data collection focused on high-value areas, achieving sufficient map information completeness for autonomous driving without the excessive communication costs of universal data collection.
4Loss of information
If data collection is performed on all road sections, then map information completeness is improved, but productivity decreases
Solution Approach 1:
The patent applies local quality by selectively updating map information only in specific road sections where it provides the greatest benefit. Instead of uniform updates across all roads, the system identifies and prioritizes particular sections based on criteria such as autonomous driving availability, traffic volume, and driver convenience improvement, thereby optimizing resource allocation.
Solution Approach 2:
The patent changes the parameter of map update priority by introducing a scoring mechanism that evaluates road sections based on multiple factors including autonomous driving availability, traffic volume, and driver convenience. This parameter transformation allows the system to dynamically prioritize which sections deserve update resources.
Data Source
AI summary
An apparatus for determining sections for map update includes a processor configured to select, for each of pairs of two locations selected from among a plurality of locations at which vehicles can enter or exit a predetermined region, a series of road sections connecting the two locations from among a plurality of road sections as a route between the two locations, calculate, for each of the pairs, a score indicating the degree of improvement of drivers' convenience provided by generating or updating map information, based on one or more candidate sections where the map information is unavailable among individual road sections included in the route between the two locations, and identify the candidate sections included in the route of each of a predetermined number of pairs among the pairs of two locations in descending order of the score as target road sections for generating or updating the map information.