Attention Detection System for Dynamic Inattentive Warning
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Solution Overview
Problem
Conventional attention allocation models fail to accurately predict operator perception in complex environments and react to unperceived events, especially in dynamic situations, leading to potential erratic actions.
Innovation Solution
A method and apparatus that utilize eye movement analysis and surround feature detection to determine an operator's attention state, providing inattentive warnings by calculating attention probability and deficiency levels based on facial images and environmental data, and issuing warnings through a guidance device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional attention allocation models are used to predict operator attention, then the system is simple to implement, but the prediction accuracy for dynamic environments is insufficient
Solution Approach 1:
The patent combines multiple data sources (eye movement data from cameras, environmental data from sensors, vehicle state data) and multiple analysis methods (eye movement analysis, environmental feature analysis, machine learning models) into a unified attention prediction system. This integration improves prediction accuracy by considering both operator behavior and environmental context simultaneously.
Solution Approach 2:
The patent introduces intermediate processing components including eye movement detection cameras, environmental sensors, data processing units, and machine learning models that act as intermediaries between raw data and final attention prediction. These intermediaries transform and analyze data to bridge the gap between simple input and accurate output.
2Adaptability or versatility
If conventional approaches focus on static areas of interest, then the analysis is straightforward, but the system cannot predict operator reaction to unperceived dynamic events
Solution Approach 1:
The patent performs preliminary analysis of the environment by identifying potential areas of interest and assessing their relevance and priority before the operator actually perceives them. The system proactively monitors dynamic elements in the environment and prepares predictions about operator attention allocation to these elements in advance.
Solution Approach 2:
The patent transitions from static area of interest analysis to dynamic environmental monitoring. The system continuously updates environmental data, reidentifies areas of interest, and adapts predictions based on changing conditions. The machine learning models are trained to predict attention to dynamic events rather than fixed locations.
3Area of stationary object
If the system monitors multiple dynamic areas of interest with high relevance and priority, then the coverage is comprehensive, but the computational complexity increases
Solution Approach 1:
The patent applies different analysis depths and processing resources to different areas of interest based on their relevance and priority values. High-priority areas receive more detailed analysis and computational resources, while lower-priority areas receive streamlined processing. This selective approach maintains comprehensive coverage while managing computational complexity.
Data Source
AI summary
A method and apparatus for determining an inattentive state of an operator of a vehicle and for providing information to the operator of the vehicle by obtaining face images of the operator of the vehicle, obtaining images of an environment of the vehicle, determining one or more areas of interest in the environment of the vehicle based on the images of the environment, obtaining, from a relevance and priority database, relevance and priority values corresponding to the one or more areas of interest, determining a probability of attention of the operator of the vehicle to the one or more areas of interest based on the images of the environment and the relevance and priority values, determining an attention deficiency based on the determined probability of attention and the face images, and providing the information to the operator of the vehicle based on the determined attention deficiency.