Driver Wakefulness Estimation Using Eye Behavior and Personal Baselines
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Solution Overview
Problem
Existing systems for monitoring a driver's state in vehicles are unable to promptly detect and respond to sudden physical abnormalities during driving, despite being capable of regular health state recognition.
Innovation Solution
An information processing apparatus and method that acquire biological information from the driver, evaluate their wakefulness degree by analyzing eye behavior and applying a wakefulness state evaluation dictionary, and estimate the time until the driver can safely resume manual driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If regular health state monitoring is implemented using body information sensors, then driver health state recognition is improved, but sudden physical abnormality detection capability deteriorates
Solution Approach 1:
The monitoring system is segmented into multiple independent monitoring modules: vital sign monitoring (heart rate, blood pressure), eye behavior monitoring (blink frequency, gaze direction), and operation information monitoring (steering input, pedal usage). Each module independently monitors specific parameters and can detect abnormalities in its domain, allowing sudden abnormalities to be detected through any single module without waiting for comprehensive health state analysis.
Solution Approach 2:
The system performs preliminary action by continuously monitoring multiple parameters in parallel and maintaining a driver state evaluation dictionary with baseline data. When an abnormality occurs, the system immediately compares current readings against pre-established thresholds and baseline patterns, enabling instant detection without requiring gradual health state analysis. The system is prepared in advance with multiple detection pathways activated simultaneously.
2Reliability
If comprehensive driver state monitoring is performed, then driver safety assessment is improved, but system complexity increases
Solution Approach 1:
The information processing apparatus performs multiple functions using a unified architecture: it monitors vital signs, analyzes eye behavior, processes operation information, evaluates wakefulness degree, detects abnormalities, and generates warnings. This multi-functional design consolidates what could be separate complex systems into a single integrated apparatus, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system merges multiple monitoring functions (vital sign monitoring, eye behavior monitoring, operation information monitoring) and data processing tasks into a single information processing apparatus. By combining these functions rather than implementing them as separate independent systems, the overall system complexity is reduced while maintaining comprehensive driver state monitoring capability.
3Measurement precision
If wakefulness degree evaluation based on eye behavior analysis is implemented, then driver alertness assessment is improved, but processing time increases
Solution Approach 1:
The system extracts specific key parameters from complex eye behavior data that are most indicative of wakefulness: blink frequency, gaze direction, and pupil diameter. By focusing on these extracted key parameters rather than analyzing all possible eye movement characteristics, the system achieves accurate wakefulness assessment while minimizing processing time. Irrelevant or less important eye behavior parameters are excluded from analysis.
Data Source
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AI summary
A configuration is realized in which driver's biological information is input and a driver's wakefulness degree is evaluated. The wakefulness degree of the driver is evaluated by applying a result of behavior analysis of at least one of an eyeball or a pupil of the driver and a wakefulness state evaluation dictionary specific for the driver. The data processing unit evaluates the wakefulness degree of the driver by using the wakefulness state evaluation dictionary specific for the driver generated as a result of learning processing based on log data of the driver's biological information. Moreover, a return time before the driver is able to start safety manual driving is estimated. A learning device used for estimation processing based on observable information is able to correlate an observable eyeball behavior of the driver and the wakefulness degree by a multidimensional factor by continuously using the learning device. By using secondary information, an index of an activity in a brain of the driver is able to be derived from a long-term fluctuation of the observable value.