Precise Map Interpolation by Road Risk and Vehicle Distance
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Solution Overview
Problem
The existing autonomous driving systems face challenges in efficiently generating and storing precise map data due to the large volume of data required, which leads to performance degradation in location determination and control when interpolation is applied.
Innovation Solution
An autonomous driving apparatus that classifies roads based on accident risk and distance from the host vehicle, varying the application ratio of interpolation and storage of precise map data to optimize storage space and reduce computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If interpolation is applied to precise map data, then storage space requirement is reduced, but location determination performance degrades
Solution Approach 1:
The patent applies different data processing strategies to different spatial regions: high-precision original data is stored for locations within a predetermined distance from the vehicle, while interpolation is applied only to locations beyond this distance. This local differentiation resolves the contradiction by preserving measurement precision where needed while reducing storage requirements in less critical regions.
Solution Approach 2:
The patent segments the precise map data into multiple groups based on distance from the vehicle (first group within predetermined distance, second group beyond predetermined distance). This segmentation allows selective application of interpolation to specific segments, thereby reducing overall storage requirements while maintaining high precision in the most critical segment near the vehicle.
2Quantity of substance
If interpolation is applied to precise map data, then data volume is reduced, but control performance degrades
Solution Approach 1:
The patent applies different data processing strategies to different spatial regions: high-precision original data is stored for locations within a predetermined distance from the vehicle, while interpolation is applied only to locations beyond this distance. This local differentiation resolves the contradiction by preserving control performance where needed while reducing data volume in less critical regions.
Solution Approach 2:
The patent segments the precise map data into multiple groups based on distance from the vehicle. This segmentation allows selective application of interpolation to specific segments, thereby reducing overall data volume while maintaining high reliability in the most critical segment near the vehicle where control decisions are most sensitive.
3Measurement precision
If all precise map data is stored in detail, then location determination accuracy is improved, but power consumption increases
Solution Approach 1:
The patent segments precise map data into multiple groups based on distance from the vehicle and applies selective storage strategies: detailed original data is stored for the first group within predetermined distance, while the second group beyond predetermined distance uses interpolation. This segmentation reduces overall data volume and power consumption while maintaining location determination accuracy for the critical near-field region.
Solution Approach 2:
The patent applies different data processing strategies to different spatial regions, storing high-precision data locally where the vehicle operates and using interpolation for distant regions. This local quality approach minimizes power consumption by reducing total data volume while preserving accuracy where the vehicle actually needs to determine its position.
Data Source
AI summary
An autonomous driving apparatus and a method for generating a precise map using the autonomous driving apparatus are configured to vary an application ratio of interpolation to precise map data to be stored depending on an accident risk on a corresponding road and a distance from a host vehicle during driving so as to optimize the precise map data to be stored. The autonomous driving apparatus includes an accident risk classification unit configured to vary the application ratio of interpolation to data and whether or not to store data acquired by applying interpolation to the data depending on the accident risk on the corresponding road and the distance from the host vehicle during driving, and an autonomous driving controller configured to generate the precise map data depending on the accident risk classification unit.