Driver Concentration Estimation for Adaptive Attention Calling
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Solution Overview
Problem
Existing driving support systems provide attention notifications at inappropriate times, either too frequently or not at all, which can be distracting and compromise driving safety, especially when a driver is focused on maintaining a safe inter-vehicle distance.
Innovation Solution
A database apparatus that estimates a driver's condition and concentration level by analyzing their operation sequence and eye distribution patterns, storing only data from focused driving operations, and providing notifications only when necessary to ensure safe driving practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If attention calling notification is provided based on comparison with standard driving model, then driving safety is improved, but false notifications occur too frequently causing driver distraction and annoyance
Solution Approach 1:
The system changes the parameter of data selection by introducing concentration degree as a filtering criterion. Only driving data with high concentration degree (above threshold) is stored in the storage unit, transforming the system from storing all driving data to storing only high-quality focused driving data, thereby reducing false notifications
Solution Approach 2:
The system extracts and stores only the essential high-quality driving data characterized by high concentration degree, separating it from low-quality data. This extraction process allows the system to build a more accurate standard driving model from purified data, reducing unnecessary notifications while maintaining safety
2Loss of information
If all driving operation data is stored for building standard driving model, then model completeness is improved, but data storage efficiency deteriorates and estimation accuracy is reduced
Solution Approach 1:
The system changes the selection parameter from storing all driving data to storing only data with high concentration degree. This parameter change ensures that the stored data represents genuine focused driving behavior, improving both the quality and accuracy of the standard driving model without sacrificing essential information
Solution Approach 2:
Instead of storing all driving data (excessive action), the system selectively stores only high-quality focused driving data (partial action). This partial storage approach is sufficient to build an accurate standard driving model while improving estimation accuracy by excluding noisy low-concentration data
Data Source
AI summary
An attention calling apparatus operates in the following manner. That is, eyes distribution pattern is generated from the behavior of the eyes of the vehicle driver captured by a camera in a predetermined time, and driver's condition is estimated based on the distribution pattern. Further, concentration of the driver on driving is estimated based on the comparison between the distribution pattern and the stored information of distribution pattern. When the distribution pattern indicates that the degree of concentration on driving is high, the driving operation is stored in storage in association with the concentration degree. When the operation situation fulfills a notification provision condition, driver's attention is called depending on the concentration degree.