Cognitive Function Estimation Using Multi-Source Driver Data
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Solution Overview
Problem
Conventional driving assistance techniques fail to accurately estimate a driver's cognitive function for safe vehicle operation, as they primarily assess temporary concentration levels rather than the driver's overall ability to operate the vehicle appropriately.
Innovation Solution
A cognitive function estimation device that acquires and processes vehicle outside information, face information, and biological information using machine learning models to determine if a driver has a certain level of cognitive function, incorporating feature extraction units for vehicle outside, face, and biological information to estimate cognitive function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional driving assistance techniques are used to estimate driver concentration, then temporary concentration level can be assessed, but the driver's overall cognitive function for safe vehicle operation cannot be accurately estimated
Solution Approach 1:
The patent segments the cognitive function assessment into multiple independent measurement dimensions: eye movement analysis, head pose estimation, facial expression recognition, and driving behavior monitoring. Each dimension is processed by dedicated feature extraction units that independently analyze specific aspects of driver state, with results integrated by the cognitive function estimation unit to provide comprehensive assessment.
Solution Approach 2:
The patent creates a multi-functional estimation system where a single cognitive function estimation device performs multiple assessment tasks simultaneously: evaluating concentration level, detecting drowsiness, assessing reaction time, and monitoring overall cognitive state. The system handles diverse data types (visual, physiological, behavioral) through unified processing architecture.
2Measurement precision
If multiple types of information (vehicle outside information, face information, biological information) are acquired and processed, then cognitive function estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the information processing system into distinct functional modules: vehicle outside information acquisition unit, face information acquisition unit, biological information acquisition unit, and corresponding feature extraction units for each data type. This segmentation allows independent optimization of each module while maintaining overall system coherence through standardized data formats and unified estimation architecture.
Data Source
AI summary
Provided are a vehicle outside information acquiring unit to acquire vehicle outside information, a face information acquiring unit to acquire face information, a biological information acquiring unit to acquire biological information, a vehicle information acquiring unit to acquire vehicle information, a vehicle outside information feature amount extracting unit to extract a vehicle outside information feature amount on the basis of the vehicle outside information, a face information feature amount extracting unit to extract a face information feature amount in accordance with the vehicle outside information feature amount, a biological information feature amount extracting unit to extract a biological information feature amount in accordance with the vehicle outside information feature amount, a vehicle information feature amount extracting unit to extract a vehicle information feature amount in accordance with the vehicle outside information feature amount, and a cognitive function estimation unit to estimate whether a cognitive function of a driver is low on the basis of a machine learning model, the vehicle outside information feature amount, and at least one of the face information feature amount, the biological information feature amount, or the vehicle information feature amount.