Vehicle Localization Method Selection via Environmental Parameter Mapping
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Solution Overview
Problem
Existing methods for determining a suitable combination of localization methods for vehicles in a target travel environment are time-consuming and costly, as they require actual equipment testing to achieve target localization accuracy, and the accuracy of localization methods varies by environment, making it difficult to predict without prior testing.
Innovation Solution
A determining system that creates a database of localization accuracy influence parameters, allowing for the prediction of localization accuracy without actual testing, by mapping and storing parameters such as canopy openness, landmark intervals, and building wall positions, enabling the selection of a suitable combination of localization methods based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual equipment testing is conducted to determine suitable localization method combination, then localization accuracy can be verified, but time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-collecting environment data (canopy openness, landmark intervals, building wall positions) and pre-calculating localization accuracy predictions for different localization methods before actual deployment. This allows the determination of suitable localization method combinations without conducting time-consuming actual equipment testing, as the predictions are already available from the database queries and calculations performed in advance.
Solution Approach 2:
The system creates a virtual model or copy of the target travel environment by collecting and storing environment data (canopy openness values, landmark interval distances, building wall position information) in a database. This environmental copy is then used to predict localization accuracy without needing to physically test actual equipment in the real environment, thereby reducing testing time while maintaining prediction reliability.
2Measurement precision
If actual equipment testing is conducted to determine suitable localization method combination, then localization accuracy can be verified, but cost increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-collecting environment data (canopy openness, landmark intervals, building wall positions) and pre-calculating localization accuracy predictions for different localization methods before actual deployment. This allows the determination of suitable localization method combinations without conducting time-consuming actual equipment testing, as the predictions are already available from the database queries and calculations performed in advance.
Solution Approach 2:
The system creates a virtual model or copy of the target travel environment by collecting and storing environment data (canopy openness values, landmark interval distances, building wall position information) in a database. This environmental copy is then used to predict localization accuracy without needing to physically test actual equipment in the real environment, thereby reducing testing time while maintaining prediction reliability.
3Device complexity
If GPS alone is used for localization, then system complexity is reduced, but localization accuracy in certain environments deteriorates
Solution Approach 1:
The system dynamically changes the parameter of localization method selection based on environmental parameters (canopy openness, landmark intervals, building wall positions). By querying the database with these environmental parameters, the system determines the most suitable localization method or combination for the current environment, thereby maintaining high localization accuracy without unnecessarily increasing system complexity when a single method suffices.
Solution Approach 2:
The system creates a universal determination mechanism that can handle multiple localization methods (GPS, landmark-based, etc.) and automatically selects the appropriate one based on environmental conditions. This multi-functional approach allows the system to maintain simplicity by using a single method when appropriate while being capable of switching to combinations when needed, thus adapting to different environments without permanently increasing complexity.
Data Source
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AI summary
A determining system (1) for localization methods combination which determines a combination of a plurality of localization methods used in a vehicle (18) includes: a unit (7) that stores therein a localization accuracy influence parameter which is determined for each position in a travel environment; a unit that stores therein a relation between the localization accuracy influence parameter and a localization accuracy of each of a plurality of the localization methods; a unit (6) that stores therein correspondence information between the localization accuracy influence parameter and each of the localization methods; a unit that acquires the localization accuracy influence parameter in the travel environment; and a unit that acquires the correspondence information on the localization accuracy influence parameter, references the relation based on the correspondence information, and thereby predicts a localization accuracy of each of the localization methods at each position in the travel environment.