Visual Allocation Management for Driver State Prediction
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Solution Overview
Problem
Existing systems fail to effectively manage and predict visual allocation in dynamic environments, particularly for drivers, as they do not consider broad visual behavior patterns and non-observable states, limiting real-time feedback and safety enhancements.
Innovation Solution
A visual allocation management system that uses captured visual data and contextual information to identify human states through mathematical and statistical models, such as Hidden Markov Models, to provide real-time feedback and adjust driver attention and vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual attention is measured based on total time directed away from the forward roadway, then measurement simplicity is improved, but measurement precision deteriorates because it fails to capture broad visual behavior patterns and non-observable states
Solution Approach 1:
The patent segments visual behavior measurement into multiple components: glance direction, glance duration, and glance transitions. Each component is measured and analyzed separately using Hidden Markov Models to infer different aspects of driver state, providing comprehensive assessment without requiring complex integrated measurement systems
Solution Approach 2:
The patent introduces Hidden Markov Models as an intermediary layer between raw visual data and driver state interpretation. The HMMs infer non-observable states (attention, awareness, emotions) from observable visual allocation patterns, enabling precise measurement of internal states without direct observation
2Reliability
If real-time feedback is provided based on visual allocation assessment, then driver safety is improved, but system complexity increases due to the need for continuous monitoring and state prediction
Solution Approach 1:
The patent pre-trains Hidden Markov Models with visual behavior data collected during various driving tasks and states. These pre-trained models can rapidly infer driver states in real-time without requiring complex runtime computation, reducing system complexity while maintaining safety
Solution Approach 2:
The patent implements continuous feedback loops where visual allocation data is constantly monitored, driver states are inferred using HMMs, and feedback is provided to maintain safe driving. The system adapts to changing driver states and environmental conditions, improving safety through dynamic adjustment
3Measurement precision
If broad visual behavior patterns are considered across extended periods, then driver state prediction accuracy is improved, but data processing time increases
Solution Approach 1:
The patent analyzes visual behavior in periodic segments rather than continuously processing all data at once. Hidden Markov Models process visual allocation patterns in defined time windows, balancing comprehensive analysis with computational efficiency to reduce processing time while maintaining prediction accuracy
Data Source
AI summary
Systems and methods for managing visual allocation are provided herein that use models to determine states based on visual data and, based thereon, output feedback based on the determined states. Visual data is initially obtained by a visual allocation management system. The visual data includes eye image sequences of a person in a particular state, such as engaging in a task or activity. Visual features can be identified from the visual data, such that glance information including direction and duration can be calculated. The visual data, information derived therefrom, and/or other contextual data is input into the models, which correspond to states, to calculate probabilities that the particular state that the person is engaged in is one of the modeled states. Based on the state identified as having the highest probability, an optimal feedback, such as a warning or instruction, can be output to a connected devices, systems, or objects.