Driver Eyeball Monitoring With Dynamic Frame-Rate Switching
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Solution Overview
Problem
High-speed sampling and analysis of eyeball behavior for accurate driver awakening level determination in autonomous driving systems lead to increased power consumption, device temperature, and decreased detection sensitivity due to high load in imaging and analysis processing.
Innovation Solution
The system dynamically switches analysis modes based on driving modes, performing high-frame-rate analysis only during mode switching events and reducing frame rate during passive monitoring, thereby reducing processing load and maintaining detection sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frame-rate sampling and analysis is performed constantly to detect eyeball behavior accurately, then measurement precision of driver awakening level is improved, but use of energy and device temperature increase
Solution Approach 1:
The system performs high-frame-rate eyeball behavior analysis only during periodic intervals when mode switching is detected or anticipated, rather than continuously. The control unit switches between first control mode (high frame rate) and second control mode (low frame rate) based on driving mode transitions, thereby reducing overall power consumption while maintaining detection accuracy when needed
2Measurement precision
If high-frame-rate sampling is performed constantly to detect eyeball behavior accurately, then measurement precision is improved, but device temperature increases and detection sensitivity decreases
Solution Approach 1:
The system alternates between high-frame-rate analysis periods (first control mode) and low-frame-rate periods (second control mode). High frame rate is applied only when mode switching events occur or are anticipated, keeping the device cool during normal operation while maintaining detection sensitivity through periodic high-resolution sampling
3Productivity
If high-frame-rate analysis is performed constantly to detect eyeball behavior accurately, then productivity of driver monitoring is improved, but loss of energy increases
Solution Approach 1:
The system implements periodic high-frame-rate analysis triggered by mode switching events rather than continuous monitoring. The control unit dynamically adjusts frame rate based on whether the vehicle is in manual or autonomous driving mode, maximizing monitoring efficiency during critical transitions while minimizing energy loss during stable autonomous operation
Solution Approach 2:
The system dynamically adapts the analysis frame rate based on the current driving mode. During manual driving or mode transition periods, the system operates in first control mode with high frame rate for maximum monitoring productivity. During stable autonomous driving, it switches to second control mode with lower frame rate, reducing energy loss while maintaining adequate monitoring capability
Data Source
AI summary
There is provided an information processing apparatus including an eyeball behavior analysis unit (300) that analyzes an eyeball behavior of a driver who drives a moving object, in which the eyeball behavior analysis unit dynamically switches an analysis mode according to a driving mode of the moving object.