Cognitive State Vehicle Control Using Near-Infrared Image Processing
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Solution Overview
Problem
Current transportation systems face challenges in managing cognitive states of vehicle occupants, leading to increased stress, safety risks, and inefficiencies due to factors like traffic congestion, unfamiliar environments, and driver fatigue, which existing technologies fail to adequately address.
Innovation Solution
The implementation of cognitive state-based vehicle manipulation using near-infrared image processing, which involves collecting images of vehicle occupants, training classifiers to determine cognitive states, and modifying them based on near-infrared content to analyze and respond to the occupant's mental and emotional states to optimize vehicle operations and route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cognitive state analysis is implemented using visible light images only, then the system complexity is reduced, but the measurement precision and reliability of cognitive state detection deteriorates
Solution Approach 1:
The patent combines visible light imaging and near-infrared imaging into a unified cognitive state analysis system. The classifier integrates features from both imaging modalities to detect cognitive states, leveraging the complementary strengths of each type of image data to improve overall detection precision while managing system complexity through integrated processing.
Solution Approach 2:
The patent uses a composite approach by combining data from two different imaging 'materials' - visible light images and near-infrared images. The classifier processes fused features from both sources, creating a composite cognitive state detection system that achieves higher precision than either modality alone.
2Reliability
If multiple imaging modalities (visible light and near-infrared) are integrated, then the reliability and precision of cognitive state detection is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal classifier that handles both visible light and near-infrared image data through a unified processing framework. This multi-functional system can process different imaging modalities using the same computational architecture, improving reliability through diverse data inputs while controlling complexity through standardized processing methods.
Solution Approach 2:
The patent introduces a feature fusion mechanism as an intermediary between the two imaging modalities and the cognitive state classification. This mediator processes and integrates features from both visible light and near-infrared images, enabling reliable multi-modal detection while managing system complexity through structured intermediate representation.
3Reliability
If real-time cognitive state monitoring is implemented, then road safety and driver capability assessment are improved, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary action by pre-training the classifier with extensive visible light and near-infrared image data before deployment. This pre-processing and pre-training phase enables the system to make rapid real-time predictions during actual operation, improving road safety through accurate monitoring while minimizing processing time during critical driving situations.
Solution Approach 2:
The patent implements partial action by focusing the full multi-modal analysis on key moments or suspicious states rather than continuously processing all data at maximum depth. This allows real-time safety monitoring with reduced computational load by applying intensive processing only when necessary based on preliminary detection.
Data Source
AI summary
Cognitive state-based vehicle manipulation uses near-infrared image processing. Images of a vehicle occupant are obtained using imaging devices within a vehicle. The images include facial data of the vehicle occupant. The images include visible light-based images and near-infrared based images. A classifier is trained based on the visible light content of the images to determine cognitive state data for the vehicle occupant. The classifier is modified based on the near-infrared image content. The modified classifier is deployed for analysis of additional images of the vehicle occupant, where the additional images are near-infrared based images. The additional images are analyzed to determine a cognitive state. The vehicle is manipulated based on the cognitive state that was analyzed. The cognitive state is rendered on a display located within the vehicle.