In-Cabin Image Classification for Driver Distraction Risk Scoring
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Solution Overview
Problem
There is a need for apparatuses, systems, and methods to automatically classify images of occupants inside a vehicle and generate data representative of vehicle in-cabin insurance risk evaluations based on skeletal diagrams indicative of driver distractions.
Innovation Solution
A vehicle in-cabin imaging device equipped with a processor and memory that stores previously classified image data, along with sensors to generate current image data. This device includes a current image classification module that compares current image data with previously classified data to classify current images and generate data for insurance risk evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image classification is used for vehicle interior monitoring, then classification accuracy can be maintained, but driver distraction increases and processing time increases
Solution Approach 1:
The system performs preliminary classification of vehicle interior images into categories (driver alone, driver with passenger, passenger alone) before detailed analysis. This preliminary action reduces the complexity of subsequent processing and enables faster decision-making about when detailed classification is needed, thereby reducing driver distraction while maintaining accuracy.
Solution Approach 2:
The system uses automated image classification algorithms that operate independently without requiring manual intervention. The processor automatically analyzes images from interior cameras, compares them against training data, and generates classifications, eliminating the need for manual review and reducing driver burden.
2Measurement precision
If detailed image analysis is performed to reduce classification errors, then measurement precision improves, but processing time increases
Solution Approach 1:
The image classification process is segmented into multiple stages: initial rapid classification into broad categories, followed by selective detailed analysis only when needed. This segmentation allows the system to process most images quickly while applying more computationally intensive analysis only to cases requiring higher precision.
Solution Approach 2:
The system applies partial analysis to most images (quick categorization) and reserves full detailed analysis for specific cases where higher precision is required. This approach processes the majority of images with minimal computational overhead while maintaining high overall accuracy through selective detailed examination.
Data Source
AI summary
The present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle by comparing current image data to previously classified image data.