Vehicle Map Updating With Accuracy-Weighted Feature Data
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Solution Overview
Problem
Existing map updating systems face challenges in maintaining high positional accuracy of features due to varying positional accuracy of data collected from vehicles, which can degrade the overall map precision.
Innovation Solution
An apparatus and method that assesses the positional accuracy of feature data collected from vehicles by comparing it to reference positions, adjusting the contribution of data based on accuracy, and applying correction information to improve positional accuracy in the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If feature data from all vehicles is used for map updating regardless of positional accuracy, then the quantity of data for map updating is increased, but the positional accuracy of features in the map deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the treatment of feature data based on the positional accuracy characteristics of individual vehicles. Each vehicle's data is evaluated separately, and contribution weights are assigned locally according to each vehicle's accuracy level rather than treating all data uniformly. This resolves the contradiction by allowing high-accuracy vehicle data to contribute fully while limiting low-accuracy data contribution.
Solution Approach 2:
The patent changes the parameter of data contribution weight based on the measured positional accuracy of each vehicle. By dynamically adjusting the contribution parameter according to accuracy measurements, the system optimizes map update quality while utilizing data from multiple vehicles. High-accuracy vehicles receive higher contribution weights, while low-accuracy vehicles receive reduced weights.
2Adaptability or versatility
If feature data with low positional accuracy is used for map updating, then the diversity of data sources is increased, but the reliability of map information deteriorates
Solution Approach 1:
The patent implements dynamics by making the contribution weight of each vehicle's data dynamic rather than static. The system continuously measures positional accuracy and adjusts contribution weights accordingly, allowing the data source reliability to adapt over time. Vehicles that consistently provide high-accuracy data automatically receive higher weights, while those with poor accuracy receive reduced weights.
Solution Approach 2:
The patent applies feedback by measuring the positional accuracy of feature data from each vehicle and using this measurement to adjust the contribution weight for subsequent map updates. This closed-loop feedback mechanism ensures that reliable data sources are rewarded with higher contribution weights while unreliable sources are penalized, maintaining overall map information reliability.
3Ease of operation
If contribution of feature data is uniformly set for all vehicles, then the ease of operation is increased, but the positional accuracy of updated map deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to automatically evaluate and weight vehicle data based on measured positional accuracy without requiring manual intervention. The system autonomously identifies high-accuracy versus low-accuracy data sources and adjusts contribution weights accordingly, maintaining simplicity while improving accuracy through automated differentiation.
Data Source
AI summary
The present disclosure is directed to improved map updating. Feature data is received from a vehicle traveling on a predetermined road section for a feature in a road section related to travel of vehicles. The feature data indicates a position of the feature. An accuracy of the position of the feature is measured based on a difference between the position of the feature and a reference position of a corresponding feature. A determination is made whether the accuracy satisfies a predetermined accuracy condition, and a contribution of the feature data is set to update of map information indicating the position of the feature. The contribution is adjusted based on whether the accuracy satisfied the accuracy condition.