Vehicle System Predicting User Needs Using Machine Learning
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Solution Overview
Problem
Current remote command systems for vehicles lack the ability to anticipate and respond to user needs based on vehicle conditions and historical data, leading to inefficient operations such as unnecessary engine starts or inadequate preparation for trips.
Innovation Solution
A method and system that utilize a machine learning model to predict user needs by analyzing vehicle data and historical operation data, providing instructions for actions like engine starts, environmental control, and notifications for fuel or battery charging based on predicted usage and destination requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If remote commands are executed without prediction, then system complexity is low, but user convenience and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting future user needs and executing vehicle preparations in advance. The machine learning model analyzes historical driving data to forecast when the user will next use the vehicle, then automatically starts the engine and adjusts environmental controls before the user arrives, eliminating the need for manual remote commands.
Solution Approach 2:
The system enables self-service by allowing the vehicle to automatically prepare itself based on predicted user needs. The machine learning model autonomously determines when preparation is needed and executes appropriate actions without requiring user intervention or complex remote command interfaces.
2Ease of operation
If engine is started frequently to ensure readiness, then user convenience improves, but energy consumption increases
Solution Approach 1:
The system performs engine start and environmental control activation only when prediction indicates the user will soon use the vehicle. By analyzing driving history and current conditions, the model timing preparations to coincide with actual user needs, avoiding unnecessary energy consumption from premature or excessive engine starts.
Solution Approach 2:
The system dynamically adjusts engine start decisions based on changing parameters including current temperature, predicted user arrival time, and historical driving patterns. The machine learning model continuously evaluates multiple parameters to determine the optimal moment for engine activation, balancing readiness with energy efficiency.
Data Source
AI summary
In an exemplary embodiment, a vehicle system is provided that includes a sensor, a memory, and a processor. The sensor is configured to at least facilitate obtaining vehicle data pertaining to one or more conditions of the vehicle. The memory is configured to at least facilitate storing historical data pertaining to a user's operation of the vehicle. The processor is coupled to the sensor and the memory, and is configured to at least facilitate: (i) generating one or more predictions of one or more needs for the user, using the vehicle data and the historical data as inputs for a machine learning model; and (ii) providing instructions to implement a vehicle action that accomplishes the one or more needs for the user based on the generated predictions via the machine learning model.