Hybrid Rule Engine for Personalized Vehicle Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle automation systems face challenges in accurately predicting user behavior and preferences, leading to inconsistent automated actions due to the need for comprehensive rule definition by users, which can be difficult when scenarios and actions are not well-defined.
Innovation Solution
A hybrid rule engine that combines deterministic elements with machine learning elements, allowing users to define rules with learned conditions and actions, where a machine learning model is trained based on user data to adapt automation routines to individual preferences without requiring explicit scenario and action definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to automate in-vehicle features, then automation capability is improved, but prediction accuracy deteriorates due to wide variations in user behavior and preferences
Solution Approach 1:
The patent combines machine learning models with pre-defined rules to create a hybrid automation system. The machine learning component handles general pattern recognition while pre-defined rules capture specific user preferences, achieving both high-level automation and accurate prediction of individual user behavior.
Solution Approach 2:
The patent segments the automation system into multiple independent rule sets, each handling specific aspects of user behavior. This allows the system to break down complex prediction tasks into manageable segments that can be individually optimized and combined.
2Adaptability or versatility
If pre-defined rules are used for each user, then customization to individual preferences is improved, but ease of operation deteriorates because users must define specific conditions and actions
Solution Approach 1:
The system automatically generates pre-defined rules based on observed user behavior patterns. Instead of requiring users to manually define rules, the system serves itself by learning from user interactions and automatically creating personalized rule sets that adapt to individual preferences.
Solution Approach 2:
The system incorporates feedback loops where user responses to automated actions are continuously monitored and used to refine and update pre-defined rules. This allows the system to improve its customization accuracy over time without requiring additional user input for rule definition.
3Reliability
If comprehensive rule definition is required, then automation accuracy is improved, but device complexity increases due to multiple elements to set and control
Solution Approach 1:
The patent creates universal rule templates that can be applied across multiple scenarios and features. Instead of requiring separate rules for each specific situation, the system uses multi-functional rule structures that can adapt to different contexts, reducing the total number of rules needed while maintaining comprehensive coverage.
Data Source
AI summary
One or more embodiments include techniques for automating vehicle routines. The techniques include receiving a rule that includes one or more deterministic elements and a machine learning element; collecting, during operation of the vehicle, a set of vehicle data based on the machine learning element; training a machine learning model that corresponds to the machine learning element using the set of vehicle data, wherein the machine learning model is used to process the machine learning element during execution of the rule.