In-Cabin Occupant Image Classification for Driver Distraction Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current vehicle systems lack effective methods for automatically classifying digital image features of occupants inside a vehicle, which is crucial for assessing driver distractions and generating insurance risk evaluations.

Innovation Solution

A vehicle in-cabin imaging device equipped with a processor, memory, and sensors that automatically classify current image features by comparing them to previously classified data, allowing for the generation of skeletal diagrams and assessment of driver distractions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If image features are manually classified, then classification accuracy can be maintained, but time consumption and operational complexity increase significantly

Engineering Contradiction:

Improveclassification speedVSAvoidclassification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system automatically classifies image features by comparing current image data with previously classified reference data stored in memory, eliminating the need for manual classification while maintaining consistency through automated pattern recognition and comparison algorithms

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Reference image data is pre-classified and stored in the system before actual classification tasks. This preliminary classification creates a database of known patterns that the system can quickly compare against new images, enabling fast automated classification without sacrificing accuracy

Inventive Principle:

Principle #10Preliminary action

2Productivity

If automated classification systems are implemented, then processing speed increases, but system complexity and computational requirements worsen

Engineering Contradiction:

Improveprocessing speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The classification system is divided into distinct functional modules: image data acquisition, feature extraction, reference data storage, comparison processing, and classification output. Each module performs a specific task, reducing overall system complexity while enabling parallel processing and improved speed

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Previously classified reference image data serves as an intermediary between raw image input and classification output. This pre-processed reference database mediates the comparison process, simplifying the computational requirements by providing ready-made patterns for matching rather than requiring complete image analysis from scratch

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12246737B2Apparatus, systems and methods for classifying digital images

Publication Date: 2025.03.11 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

The present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle by comparing current image feature data to previously classified image features.