In-Cabin State Translation Layer for AI Vehicle Control
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Solution Overview
Problem
Existing vehicular management systems lack effective methods to analyze facial expressions and in-cabin states to enhance safety and comfort by adjusting vehicle settings or transferring control to autonomous modes based on occupant conditions.
Innovation Solution
A vehicular management system with a text translation layer that processes images and sensor data to determine in-cabin states, using a generative AI facility to generate textual responses for vehicle management, including adjustments and control transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If facial expression analysis and in-cabin state monitoring are implemented to enhance safety and comfort, then road safety and occupant wellbeing are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces a text translation layer as an intermediary component that converts complex sensor data and facial expression analysis results into simplified text prompts. This mediator enables the generative AI facility to process occupant states without directly handling raw sensor data, thereby improving road safety through accurate state detection while reducing the computational burden on the system.
Solution Approach 2:
The system is divided into distinct functional modules: imaging devices for facial capture, sensor systems for in-cabin monitoring, a text translation layer for data processing, and a generative AI facility for decision-making. This segmentation allows each component to specialize in specific tasks, improving overall reliability for safety monitoring while making the complex system more manageable and maintainable.
2Speed
If real-time processing of images and sensor data is performed to determine in-cabin states, then responsiveness to occupant conditions is improved, but computational energy consumption increases
Solution Approach 1:
The text translation layer performs preliminary processing of raw sensor data and facial expression analysis results before they are passed to the generative AI facility. By pre-converting complex data into structured text prompts, the system achieves faster real-time processing of in-cabin states while reducing the computational energy required by the AI facility, as it receives pre-processed information rather than raw data.
3Adaptability or versatility
If a text translation layer with configurability parameters is used to process in-cabin states, then adaptability to different vehicle management needs is improved, but device complexity increases
Solution Approach 1:
The text translation layer incorporates configurability parameters that allow the system to adapt to different vehicle management needs by modifying processing parameters, output formats, and translation rules. These parameter changes enable the same core system to serve multiple purposes (safety monitoring, comfort optimization, control transfer) without requiring fundamentally different architectures, thus improving adaptability while keeping software complexity manageable through parameterization rather than structural complexity.
Data Source
AI summary
Techniques for managing vehicles are disclosed. One or more images are obtained of a vehicle occupant using one or more imaging devices within the vehicle. The one or more images are augmented with in-cabin sensor data. The one or more images include facial data of the vehicle occupant. A computing device is used to analyze the one or more images to determine an in-cabin state. The in-cabin state is processed using a text translation layer. The text translation layer is configured using configurability parameters. The processing outputs one or more text sentences describing the in-cabin state. A generative artificial intelligence (AI) facility is seeded using the one or more sentences. The vehicle is managed based on a textual response from the generative AI facility. The managing the vehicle includes indexing in-cabin state information. The managing the vehicle further includes providing vehicle manipulation instructions.