Vehicle Rule Generation Using LLMs for Adaptive Operation
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Solution Overview
Problem
Current vehicle operation systems require users to follow complex steps to adjust settings and lack robustness in generating complex rules, often necessitating expert intervention for new commands, and may fail when faced with higher complexity tasks.
Innovation Solution
A system utilizing sensors to collect data on vehicle and user states, combined with a large language model to generate rules based on user inputs, conditions, and events, allowing for intuitive and robust operation adjustments without expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predefined commands are used for vehicle operation, then the system is easy to operate, but the system lacks adaptability when new commands are needed
Solution Approach 1:
The system enables users to create their own custom commands without expert intervention. The natural language processing system allows users to define personalized vehicle operations through simple speech or text inputs, making the system self-configurable and adaptable to individual needs while maintaining ease of operation.
Solution Approach 2:
The command system transitions from static predefined commands to dynamic user-generated commands. The system continuously learns and adapts to user preferences, allowing commands to evolve and change based on user behavior patterns and feedback, thereby improving both adaptability and ease of operation over time.
2Reliability
If expert intervention is required for adding new commands, then the system maintains reliability, but the device complexity increases
Solution Approach 1:
The system eliminates the need for expert intervention by enabling users to independently create and manage custom commands. The natural language processing and validation systems automatically handle command creation, ensuring reliability through built-in validation rules while keeping the system simple for end-users to operate.
Solution Approach 2:
The natural language processing system acts as an intermediary between user intent and vehicle control systems. It translates casual user inputs into structured commands with automatic validation, bridging the gap between simple user interaction and reliable system execution without requiring expert users.
3Adaptability or versatility
If complex rules are generated for vehicle operation, then the adaptability improves, but the system fails to maintain robustness
Solution Approach 1:
The system incorporates validation mechanisms that provide feedback on generated rules. The natural language processing system analyzes user inputs against predefined validation criteria, ensuring that complex adaptive rules maintain robustness by filtering out invalid or conflicting commands while preserving legitimate user intentions.
Solution Approach 2:
The rule generation system dynamically adjusts complexity based on validation results. It allows users to create complex adaptive rules when needed while automatically simplifying or rejecting rules that compromise system robustness, maintaining a balance between adaptability and reliability through continuous validation and refinement.
Data Source
AI summary
A system for rule-based operation of a vehicle is provided, comprising one or more sensors configured to monitor an interior and/or an exterior of the vehicle and/or receive a user input, and to thereby collect data related to a state of the vehicle and/or a state of the surrounding of the vehicle, and an action of the user of the vehicle and/or the user input. The system comprises a processor configured to use a machine learning model, preferably a large language model, with input data based on the collected data to generate a rule for operating the vehicle. The rule includes at least one action associated with at least one condition and/or at least one event. The processor is configured to analyze the collected data to validate that the condition is met and/or that the event has occurred, and cause the vehicle to perform the action.