In-Vehicle Computing System Contextual Data Integration
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Solution Overview
Problem
In-vehicle computing systems face challenges in providing an adaptive user experience due to reliance on limited user input and fragmented data sources, which can lead to increased cognitive load and decreased safety for drivers.
Innovation Solution
An in-vehicle computing system that integrates contextual data from multiple sources, including sensors, social media networks, and external devices, to analyze driver behavior and vehicle conditions, using a data integration subsystem, analytics engine, and rules engine to adjust settings and provide targeted outputs, thereby reducing cognitive load and enhancing safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the in-vehicle computing system integrates contextual data from multiple sources (sensors, social media, external devices), then the adaptability and accuracy of user experience is improved, but the device complexity increases
Solution Approach 1:
The system segments data processing into distinct functional modules: a data integration subsystem that collects contextual data from multiple sources, an analytics engine that processes the integrated data, and a rules engine that generates control instructions. This modular segmentation allows each component to handle specific tasks independently, improving adaptability while managing complexity through organized functional division.
Solution Approach 2:
The patent introduces an external device interface as an intermediary layer between the in-vehicle computing system and external data sources (social media networks, external devices). This intermediary manages communication protocols and data formats, enabling the system to integrate diverse contextual data without directly complicating the core processing architecture.
2Ease of operation
If the system autonomously adjusts vehicle settings based on real-time contextual data, then driver cognitive load is reduced and safety is enhanced, but the extent of automation increases
Solution Approach 1:
The system continuously monitors driver cognitive load through sensor data (eye tracking, steering patterns, response time) and adjusts vehicle settings in real-time based on this feedback. When high cognitive load is detected, the rules engine automatically modifies output settings (reducing display brightness, adjusting audio levels, simplifying navigation instructions), creating a closed-loop feedback system that enhances safety while managing automation through adaptive response.
Solution Approach 2:
The system dynamically adjusts vehicle output settings based on real-time driver state rather than using fixed configurations. The analytics engine continuously processes contextual data to determine current cognitive load levels, and the rules engine dynamically modifies system parameters (display, audio, navigation) to optimize driver comfort and safety, allowing the automation level to adapt to changing operational conditions.
3Productivity
If the system processes and analyzes aggregated data from multiple sources in real-time, then the productivity of user experience delivery is improved, but the use of energy increases
Solution Approach 1:
The system performs preliminary data aggregation and filtering at the data integration subsystem before full analysis. Contextual data from multiple sources is pre-processed, validated, and organized into structured formats during periods when computational demand is lower, preparing data for rapid analysis when needed. This preliminary action reduces the real-time processing burden on the analytics engine, improving productivity while managing energy consumption through staged computation.
Data Source
AI summary
Embodiments are disclosed for systems and methods for controlling operation of an in-vehicle computing system. In some embodiments, an in-vehicle computing system includes a processor, an external device interface communicatively coupleable to an extra-vehicle server, and a storage device storing instructions executable by the processor to receive information from a navigation subsystem and one or more sensors of the vehicle. The information may include user information identifying one or more occupants of the vehicle with one or more accounts of a social media network. The instructions may also be executable to send the received information to the server, receive instructions from the server, and transmit control instructions to one or more vehicle systems based on the identified action. The instructions may identify an action to be performed based on the received information.