Vehicle Computing System Associative Rule Filter
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle computing systems lack the ability to efficiently determine and adapt to occupant preferences for various vehicle features, leading to increased user interaction time and complexity in managing menu choices during driving.
Innovation Solution
A vehicle computing system that utilizes an associative rule filter to determine occupant preferences by analyzing previous selections and context variables, allowing for automatic selection of vehicle features based on learned preferences and simplifying menu choices through a 'Choose For Me' feature, which presents a limited list of options and defaults to a predicted choice if no action is taken within a set time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the vehicle computing system presents all available menu options to the occupant, then the occupant has complete control over feature selection, but the user interaction time and complexity increase
Solution Approach 1:
The system performs preliminary analysis of occupant preferences using associative rule filters before presenting menu options. By pre-processing occupancy data, vehicle state, and historical preferences, the system prepares a filtered subset of relevant options in advance, reducing the cognitive load and interaction time required from the occupant while maintaining access to complete functionality when needed.
Solution Approach 2:
The menu system is segmented into multiple hierarchical levels. The first level presents only the most relevant options based on associative rule filtering, while additional levels provide access to complete functionality. This segmentation allows the system to reduce immediate interaction complexity while preserving full adaptability and versatility for occupants who need or want to explore all options.
2Loss of time
If the vehicle computing system automatically selects features based on learned preferences, then user interaction time is reduced, but the system complexity and learning requirements increase
Solution Approach 1:
The system implements self-service by automatically learning and adapting to occupant preferences without requiring explicit programming or complex configuration. The associative rule filter continuously processes occupancy data, vehicle state, and selection patterns to automatically generate personalized feature selections, reducing user interaction time while managing system complexity through incremental learning rather than complex upfront setup.
Solution Approach 2:
The system incorporates feedback mechanisms where occupant selections and corrections are continuously fed back into the associative rule filter. This feedback loop allows the system to refine its understanding of preferences over time, improving automatic selection accuracy while keeping the underlying system complexity manageable through iterative adaptation rather than requiring complex predictive models from the start.
3Speed
If the system filters menu options based on associative rules, then relevant options are presented faster, but the filtering algorithm complexity increases
Solution Approach 1:
The associative rule filter implements partial action by focusing on the most significant features and context variables rather than analyzing every possible parameter. The system identifies and processes only the key occupancy indicators, vehicle states, and preference patterns that have the greatest impact on feature selection, achieving fast option presentation while managing algorithm complexity through selective rather than exhaustive analysis.
Data Source
AI summary
A vehicle computing system enables one or more processors to control a plurality of vehicle features. The vehicle computing system may control a plurality of vehicle features while determining and selecting occupant preferences for those features. The vehicle computing system may receive input including occupant selections of an application requesting user control. The vehicle computing system may receive context variables at the time of occupant selections. The vehicle computing system may store in memory the occupant selections of the vehicle feature settings and/or controls with the associated context variables. The vehicle computing system may determine an occupant preference to an input request using an associative rule filter based on the occupant selections stored in memory. The vehicle computing system may assign a predetermined amount of time after the application input is requested to allow control of the vehicle feature based on an associative rule filter output.