3D Eyeball Position Detection for Accurate Driver Visual Attention
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Solution Overview
Problem
Existing alarm devices for vehicles fail to accurately notify drivers of their visual attention status due to limitations in detecting three-dimensional visual attention points, leading to poor detection accuracy and ineffective notification.
Innovation Solution
An alarm device that includes a host vehicle information acquisition unit, vehicle external information acquisition unit, visual attention target calculation unit, sight line information acquisition unit, eyeball position information acquisition unit, visual attention point calculation unit, determination unit, and notification unit, which together calculate and notify the driver of their visual attention status by determining if they are focusing on critical targets using three-dimensional coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a monocular one-dimensional camera is used to detect face or sight line orientation, then the device complexity is reduced, but the measurement precision of visual attention point deteriorates
Solution Approach 1:
The patent transitions from one-dimensional camera detection to three-dimensional coordinate system for eyeball position detection. This dimensional upgrade enables accurate calculation of visual attention points in space, resolving the precision issue while maintaining practical device complexity through efficient coordinate transformation algorithms.
Solution Approach 2:
The patent changes the detection parameters from simple face orientation angles to three-dimensional eyeball position coordinates (x, y, z). This parameter transformation enables precise determination of visual attention points by capturing the spatial position of eyeballs, thereby improving measurement precision without excessive complexity increase.
2Measurement precision
If three-dimensional eyeball position detection is implemented, then the measurement precision of visual attention point is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary calculation process that uses detected eyeball positions to compute visual attention points through coordinate transformations. This intermediary step bridges the gap between raw detection data and meaningful visual attention information, achieving high precision while managing system complexity through algorithmic processing rather than hardware complexity.
3Device complexity
If visual attention detection is not performed, then the device complexity is reduced, but the reliability of driver notification system deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the visual attention detection results directly control the notification output. The system continuously monitors driver visual attention and adjusts notifications based on detected attention status, thereby improving notification reliability through real-time feedback without excessive system complexity.
Data Source
AI summary
An alarm device includes: a host vehicle information acquisition unit; a vehicle external information acquisition unit; a visual attention target calculation unit calculating a target to which attention of a driver is required based on host vehicle information and vehicle external information; a sight line information acquisition unit acquiring sight line information about the driver; an eyeball position information acquisition unit acquiring eyeball position information about the driver; a visual attention point calculation unit calculating a point to which the driver pays attention, based on the sight line information and the eyeball position information; a determination unit determining whether the driver pays attention to the target, based on the target and the point; and a notification unit notifying the driver of a determination result when determined that the driver does not pay attention to the target.