AI Vehicle Function Automation From User-Activity Clusters
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Solution Overview
Problem
Current vehicle control systems require manual input for settings and lack personalized automation, leading to inefficiencies and increased user interaction, which can result in delayed responses and resource consumption.
Innovation Solution
A computing system utilizing machine-learned clustering models to analyze user interactions and generate automated vehicle actions based on user preferences, allowing for personalized and automated control of vehicle functions such as seat massage, ventilation, and window operations, by identifying patterns in user behavior and triggering conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual input is required for vehicle settings, then user control is maintained, but responsiveness is delayed and resource consumption increases
Solution Approach 1:
The vehicle system performs self-service by automatically learning user preferences through observation of manual interactions and autonomously executing settings without requiring repeated manual input. The system serves itself by generating automated actions from observed patterns, eliminating the need for continuous user intervention while maintaining personalized control.
Solution Approach 2:
The system performs preliminary action by pre-learning user preferences through observed manual interactions and preparing automated responses in advance. When a user manually adjusts a setting, the system learns this preference beforehand and automatically executes it in similar situations, preventing the need for repeated manual adjustment and reducing response delays.
2Productivity
If automated vehicle actions are implemented, then responsiveness is enhanced and resource usage is optimized, but system complexity increases
Solution Approach 1:
The automated vehicle action system acts as an intermediary layer between the user and vehicle functions. Instead of directly controlling vehicle systems, the learning module observes user interactions and generates automated actions that mediate between user intent and system execution, simplifying the overall control architecture while enhancing productivity.
3Adaptability or versatility
If personalized automation is introduced, then user experience is improved, but data processing requirements increase
Solution Approach 1:
The system applies local quality by personalizing automation specifically for individual users and individual vehicle functions rather than implementing global automation across all systems. The learning module focuses on specific user preferences for specific functions (e.g., seat massage, ventilation) and generates targeted automated actions, reducing overall data processing requirements while maintaining high adaptability.
Data Source
AI summary
Methods, computing systems, and technology for automated vehicle action generation are presented. For example, a computing system may be configured to receive context data associated with a plurality of user interactions with a vehicle function of a vehicle. The context data may include data indicative of a plurality of user-selected settings for the vehicle function and data indicative of observed conditions associated with the respective user-selected settings. The computing system may be configured to generate, using a machine-learned clustering model, a user-activity cluster for the vehicle function based on the context data. The computing system may be configured to determine an automated vehicle action based on the user-activity cluster. The computing system may output command instructions for the vehicle to implement the automated vehicle action for automatically controlling the vehicle function in accordance with an automated setting based on whether the vehicle detects one or more triggering conditions.