AI Vehicle Function Automation Using User-Activity Clusters
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Solution Overview
Problem
Existing vehicle systems lack the ability to efficiently and accurately automate vehicle functions based on user preferences, leading to inefficiencies in resource usage and user interaction.
Innovation Solution
A computing system utilizing machine-learned clustering models to analyze user interactions and generate automated vehicle actions, including a multi-stage, multi-model framework to process context data, determine user-activity clusters, and implement automated settings based on triggering conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If vehicle systems manually control features in response to operator input, then user customization is achieved, but computational resources are wasted due to lack of automation
Solution Approach 1:
The system performs preliminary actions by learning user preferences and predicting future needs before the user actually requests them. The machine learning models analyze historical interaction data and contextual information to proactively determine what vehicle functions the user will want, thereby automating control before manual input is needed and conserving processing resources.
Solution Approach 2:
The vehicle system serves itself by using its own historical data to learn and predict user preferences. The machine learning models continuously improve their understanding of user behavior patterns through self-analysis of interaction data, enabling the system to automatically adjust vehicle functions without requiring constant user input or extensive external processing.
2Ease of operation
If vehicle systems automate functions based on user preferences, then manual interactions are reduced, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: data collection components that gather interaction data, machine learning models that process and learn from this data, prediction components that generate future preference predictions, and execution components that implement automated vehicle function control. This modular segmentation manages system complexity by dividing the overall system into manageable, independently developable parts.
Solution Approach 2:
Machine learning models serve as intermediaries between raw user interaction data and automated vehicle control decisions. These models translate historical interaction patterns into predictive preferences that guide automated function control, acting as a mediator that simplifies the complexity by providing a structured layer of interpretation between data collection and action execution.
3Measurement precision
If contextual data is collected for accurate predictions, then personalization improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features and patterns from contextual data that are necessary for accurate preference prediction. Rather than processing all available contextual information equally, the machine learning models identify and extract key predictive features from user interactions and context, thereby maintaining personalization accuracy while reducing overall data processing requirements.
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.