Vehicle Management With Category-Specialized Position Models
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Solution Overview
Problem
Creating a machine learning model that accurately estimates the position of various vehicle types is costly and requires frequent updates due to the release of new vehicle models, necessitating a general-purpose model that is inefficient and expensive to maintain.
Innovation Solution
A vehicle management system that utilizes a target-specialized parameter from a parameter providing apparatus to specialize a machine learning model for a specific vehicle category, enabling accurate position estimation without the need for a general-purpose model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general-purpose machine learning model is created to meet all vehicle types, then the model can handle diverse vehicle categories, but the work and cost to create and update the model increases significantly
Solution Approach 1:
The patent segments the machine learning model into a base model and vehicle-category-specific modules. The base model handles common vehicle detection tasks, while separate modules are created for different vehicle categories (cars, trucks, motorcycles, etc.). This segmentation allows the system to handle diverse vehicle types without creating a single complex general-purpose model, thereby reducing overall model creation and update complexity while maintaining versatility.
2Measurement precision
If a machine learning model is trained to accurately estimate positions of all vehicle types, then position estimation accuracy improves, but the cost and work to train and update the model for each new vehicle type increases
Solution Approach 1:
The patent implements preliminary action by pre-training a base machine learning model with general vehicle detection capabilities before deployment. This base model serves as a foundation that can be quickly adapted to specific vehicle categories through parameter adjustments rather than complete retraining. When new vehicle types are introduced, the system performs preliminary parameter adjustments on the existing base model rather than training from scratch, significantly reducing the time and computational resources required for updates while maintaining high position estimation accuracy.
3Adaptability or versatility
If a general-purpose machine learning model is used to cover all vehicle categories, then no separate models are needed, but the storage requirements and computational resources increase
Solution Approach 1:
The patent merges the common detection capabilities into a single base model that is shared across all vehicle categories. Instead of maintaining separate complete models for each vehicle type, the system combines the base model with lightweight category-specific parameter sets. This merging approach significantly reduces total storage requirements compared to maintaining multiple full models, while still providing comprehensive coverage for all vehicle types through the combination of the shared base model and specialized parameters.
Data Source
AI summary
A vehicle management system manages a vehicle in a predetermined area. The vehicle management system has a machine learning model for estimating a position of a vehicle shown in an image. The vehicle management system acquires a target-specialized parameter from a parameter providing apparatus, the target-specialized parameter being a parameter of the machine learning model trained with a focus on a category of a target vehicle. The vehicle management system apples the target-specialized parameter to the machine learning model to acquire a target-specialized machine learning model specialized in the category of the target vehicle. The vehicle management system acquires an image captured by a camera installed in the predetermined area and showing the target vehicle. The vehicle management system estimates a position of the target vehicle based on the image and the target-specialized machine learning model.