Crowdsourced Pairwise Comparison for Parking Difficulty Model Training
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Solution Overview
Problem
Current parking difficulty modeling relies heavily on high-quality human annotation, which is costly and hard to access, limiting the ability to predict parking difficulty at various locations effectively.
Innovation Solution
A method for training predictive parking difficulty models using crowdsourced subjective pairwise comparisons to obtain ground truth rankings, which are then used to adjust model parameters and minimize prediction loss, allowing for improved annotation quality and extension to unlabeled locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality human annotation is used for training parking difficulty models, then measurement precision and reliability improve, but cost and data collection difficulty increase significantly
Solution Approach 1:
The patent uses crowdsourced pairwise comparisons as a proxy copy of expensive human annotation. Instead of requiring detailed expert annotations for every location, the system collects simplified comparative judgments from many users, which can be aggregated to produce reliable training data at lower cost and complexity
Solution Approach 2:
The patent replaces expensive, time-consuming expert annotation with inexpensive, quick pairwise comparisons from crowdsourced users. Each user provides a simple comparison judgment rather than a comprehensive analysis, allowing rapid data collection at minimal cost while maintaining sufficient quality through aggregation
2Adaptability or versatility
If comprehensive parking difficulty data is collected for all locations, then model coverage and accuracy improve, but annotation cost and time requirements increase
Solution Approach 1:
The patent segments the annotation task into pairwise comparisons rather than requiring comprehensive individual assessments for each location. By breaking down the complex annotation problem into simpler binary comparisons, the system enables scalable data collection across many locations without requiring extensive time investment from annotators
Solution Approach 2:
The patent collects partial annotations through pairwise comparisons rather than requiring complete annotations for all locations. The crowdsourced approach gathers sufficient comparative data to train the model effectively without needing exhaustive annotation of every possible location, achieving good coverage with less effort
3Reliability
If consistent scoring from multiple humans is required, then reliability improves, but data collection difficulty and cost increase
Solution Approach 1:
The patent merges multiple human judgments through aggregation of crowdsourced pairwise comparisons. Instead of requiring each individual to provide consistent detailed scores, the system combines simplified comparative judgments from many users to achieve reliable training data, where the collective wisdom overrides individual variations
Solution Approach 2:
The patent implements feedback mechanisms in the crowdsourced annotation process, where the system monitors and utilizes the collective feedback from multiple users to refine the training data. By aggregating responses and observing patterns across multiple humans, the system achieves consistent reliability without requiring each individual to maintain perfect consistency
Data Source
AI summary
The present disclosure relates to a concept for training one or more model parameters of a predictive parking difficulty model for different locations based on collected telemetry data. A ground truth ranking related to subjective parking difficulties at the different locations is obtained based on pairwise comparison of parking difficulties between pairs of the different locations by one or more humans. A prediction loss between a model ranking of the different locations obtained by the predictive parking difficulty model and the ground truth ranking is determined. The one or more model parameters are adjusted to minimize the prediction loss between the model ranking and the ground truth ranking.