Autonomous Vehicle Scan Data Matching via Environment Feature Segmentation
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Solution Overview
Problem
Conventional scan data matching algorithms are not optimized for autonomous driving environments, leading to poor performance in terms of computational efficiency, accuracy, and robustness.
Innovation Solution
A method that extracts features from scan data considering driving environment specifics, such as ground and non-ground data, and uses these features to match scan data, optimizing the matching process for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional scan data matching algorithms are used, then versatility across different fields is improved, but performance in terms of computational amount, accuracy, and robustness deteriorates when applied to autonomous driving
Solution Approach 1:
The patent segments scan data into ground data and non-ground data based on distance from the autonomous vehicle. This segmentation allows the algorithm to process different types of environmental features separately, improving accuracy and robustness for autonomous driving while maintaining the ability to handle various scanning scenarios through flexible data categorization.
Solution Approach 2:
The patent applies different processing characteristics to different portions of scan data based on their spatial characteristics. Ground data (closer to the vehicle) and non-ground data (farther from the vehicle) are processed with different matching algorithms and parameters, optimizing performance for the specific autonomous driving context while preserving versatility through adaptive local processing.
2Ease of manufacture
If conventional scan data matching algorithms are used, then ease of implementation is improved, but accuracy and robustness in autonomous driving environments deteriorate
Solution Approach 1:
The patent performs preliminary classification of scan data into ground and non-ground categories before matching. This preliminary action simplifies the subsequent matching process by pre-organizing data based on spatial characteristics, making the algorithm easier to implement while significantly improving matching accuracy through targeted processing of different data types.
Solution Approach 2:
The patent changes processing parameters based on the type of scan data being processed. Different matching algorithms and parameter settings are applied to ground data versus non-ground data, improving measurement precision through parameter optimization while maintaining ease of implementation through a systematic parameter selection framework.
3Productivity
If conventional scan data matching algorithms are used, then computational efficiency is reduced, but robustness in complex driving environments deteriorates
Solution Approach 1:
The patent segments scan data into ground and non-ground portions, allowing computational resources to be allocated efficiently to different data types. This segmentation improves computational efficiency by processing only relevant features while enhancing robustness in complex environments through specialized handling of different environmental conditions.
Data Source
AI summary
Provided are a method of matching scan data based on driving environment features of an autonomous vehicle, a computing device, and a recording medium. The method of matching the scan data based on the driving environment features of the autonomous vehicle according to various embodiments of the present invention that is performed by a computing device includes extracting features from a plurality of pieces of scan data in consideration of driving environment features of an autonomous vehicle and performing matching on the plurality of pieces of scan data using the extracted features.