Map Curation Management System for Reducing Manual Correction Time
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Solution Overview
Problem
The process of human curation for map data in vehicles is labor-intensive and time-consuming, as it requires manual correction of auto-curated maps, which can vary significantly in complexity from location to location.
Innovation Solution
A map curation management system that utilizes auto-curation predictive models and manual-curation time predictive models to estimate the time required for manual curation, generate heat maps, and distribute uncurated map data efficiently to human curators based on estimated manual curation times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual curation is performed to correct auto-curated maps, then map accuracy is improved, but labor intensity and time consumption increase
Solution Approach 1:
The system uses predictive models that automatically estimate manual curation time requirements and generate heat maps without human intervention. The models self-assess the complexity of map segments and predict resource needs, eliminating the need for manual time estimation and enabling automated workflow optimization
Solution Approach 2:
The system performs preliminary analysis by generating heat maps and predicting curation times before actual manual curation begins. This advance prediction allows for proactive resource allocation and workflow planning, reducing the overall time required for the curation process
2Manufacturing precision
If manual curation is performed to correct auto-curated maps, then map accuracy is improved, but labor intensity increases
Solution Approach 1:
The predictive models automatically assess map segment complexity and predict curation time requirements without human intervention. This self-assessment capability eliminates manual evaluation efforts and provides automated guidance for resource allocation, reducing labor intensity while maintaining curation quality
3Productivity
If uncurated map data is distributed to multiple curators, then productivity is improved, but coordination complexity increases
Solution Approach 1:
The system assigns different heat map segments to different curators based on predicted curation times and complexity. Each curator receives a customized workload with varying difficulty levels, allowing for optimized resource allocation and balanced workload distribution across the curation team
Solution Approach 2:
The system divides uncurated map data into discrete segments represented as heat map regions. Each segment can be independently assigned to different curators, enabling parallel processing and improving overall productivity while maintaining manageable coordination complexity through clear segment boundaries
4Productivity
If predictive models are used to estimate manual curation time, then resource allocation is improved, but system complexity increases
Solution Approach 1:
The predictive models serve as intermediaries between the raw map data and the curation workflow. These models translate complex map features into simplified predictions of curation time and complexity, enabling efficient resource allocation without requiring the rest of the system to understand the underlying complexity of map segmentation
Data Source
AI summary
System, methods, and other embodiments described herein relate to implementing map curation management strategies. In one embodiment, a method includes receiving map data, using an auto-curation predictive model to update the map data with auto-curated data, and using a manual-curation time predictive model to estimate a manual-curation time and generate a manual-curation heat map based on the map data.