Vehicle Occupant HMI Accessibility Scoring for Emergency Assistance
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Solution Overview
Problem
Conventional assistive vehicle devices fail to dynamically determine and prioritize the accessibility needs of vehicle occupants, particularly in emergency situations, and do not effectively integrate dynamic content from third-party applications to provide a uniform and non-distracting experience.
Innovation Solution
A system and method that utilize a combination of Artificial Neural Networks (ANN) and rule engines to process static and dynamic information, determining an accessibility score for vehicle occupants and generating accessibility-compliant Human Machine Interface (HMI) content and vehicle control content, which can be executed to provide assistance and prioritize messages based on severity and availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional assistive vehicle devices consider only static accessibility needs, then device complexity is reduced, but adaptability to dynamic accessibility needs deteriorates
Solution Approach 1:
The system dynamically adjusts HMI content and vehicle control based on real-time accessibility scores calculated from multiple data sources including sensor data, occupant data, and environmental data. The accessibility score and associated content are continuously updated as conditions change, allowing the system to adapt to dynamic accessibility needs rather than relying on static pre-configured settings.
Solution Approach 2:
The system implements a feedback loop where sensor data, occupant data, and environmental data are continuously collected, processed through an artificial neural network to calculate accessibility scores, and then used to adjust HMI content and vehicle control. This closed-loop feedback mechanism enables the system to respond to changing conditions and improve accessibility based on actual occupant needs.
2Loss of information
If multiple dynamic content are generated without priority management, then information completeness is improved, but information overload and distraction increase
Solution Approach 1:
The system applies different quality levels and presentation formats to different pieces of information based on their priority and the occupant's specific accessibility needs. High-priority safety-critical information is presented with prominent visual and auditory cues, while lower-priority information is presented in a more subdued manner. This localized quality adjustment ensures important information is communicated effectively without overwhelming the driver with equal treatment of all information.
Solution Approach 2:
The system continuously monitors the driver's state and environmental conditions to dynamically adjust the priority and presentation of information. Based on the calculated accessibility score and current context, the system feedback-adjusts which information is displayed, its prominence, and its timing to minimize distraction while maintaining information completeness.
3Adaptability or versatility
If HMI content is not curated for uniform viewing experience, then content variety is improved, but viewing consistency and reduced distraction deteriorate
Solution Approach 1:
The system curates HMI content by applying specific design principles tailored to the occupant's accessibility needs, such as adjusting font sizes, contrast ratios, color schemes, and layout configurations based on the calculated accessibility score. This localized quality adjustment ensures that diverse content is presented in a visually consistent and non-distracting manner appropriate for each occupant's specific needs.
Solution Approach 2:
The system enforces homogeneous visual and interaction characteristics across all HMI content by applying standardized design templates and accessibility guidelines. All content is rendered with consistent typography, spacing, color palettes, and interaction patterns that match the occupant's accessibility profile, creating a uniform viewing experience that reduces cognitive load and distraction.
4Reliability
If assistive devices cannot react to fatal conditions, then system simplicity is maintained, but emergency response capability deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously calculating accessibility scores and preparing emergency response protocols before critical events occur. The artificial neural network is trained to recognize patterns indicating deteriorating occupant conditions, and the system pre-configures appropriate emergency responses and notifications to emergency services, enabling rapid reaction when fatal conditions are detected.
Solution Approach 2:
The system implements continuous feedback monitoring of occupant vital signs and accessibility metrics to detect fatal conditions. When the accessibility score indicates a critical state, the feedback loop triggers immediate emergency responses including alerting emergency services, notifying nearby vehicles, and adjusting vehicle control systems, creating a reliable emergency response mechanism through continuous monitoring and automated feedback-driven actions.
Data Source
AI summary
A system and method for providing assistance to vehicle occupants is disclosed. The method includes the steps of receiving a set of static information and a set of dynamic information associated with each of a vehicle, a set of Human Machine Interfaces (HMIs), and an occupant within the vehicle; processing the set of static information and the set of dynamic information through at least one of a first Artificial Neural Network (ANN) Model and a rule engine; determining an accessibility score for the occupant in response to an event based on an output of the processing; generating at least one of at least one accessibility compliant HMI content for at least one of the set of HMIs and vehicle control content for the vehicle based on the accessibility score; and executing at least one of the at least one accessibility compliant HMI content and the vehicle control content.