In-Vehicle Environment Settings Across Vehicles and Weather
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Solution Overview
Problem
Existing in-vehicle environment setting systems do not consider outdoor environments and transient setting changes, leading to inconvenient temperature settings when users switch between vehicles, as they primarily focus on user preferences for steady-state conditions without accounting for external factors like weather.
Innovation Solution
An in-vehicle environment setting system that includes a receiving unit for gathering setting and outdoor environment information, a learning unit to understand user preferences, a generation unit to create recommended settings based on learned data, and a transmission unit to adjust settings in real-time across different vehicles, taking into account both steady-state and transient changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If setting information is converted to conform to a different vehicle without considering outdoor environment, then user preference consistency is improved, but setting appropriateness deteriorates
Solution Approach 1:
The system performs preliminary analysis of both user preference data and outdoor environment conditions before generating setting recommendations. By pre-processing and storing both types of information in advance, the system can quickly retrieve and combine them when a user boards a vehicle, ensuring both user preference consistency and setting appropriateness without real-time computation delays.
Solution Approach 2:
The system dynamically adjusts setting parameters based on changes in outdoor environment conditions. Instead of using fixed conversion rules, the system modifies setting parameters (such as temperature, lighting, seat position) according to environmental parameter changes, thereby maintaining setting appropriateness across different vehicles while respecting user preferences.
2Device complexity
If only steady-state settings are learned without transient setting changes, then system complexity is reduced, but user preference accuracy deteriorates
Solution Approach 1:
The system segments user preference data into two distinct components: steady-state settings and transient setting changes. By separating these two aspects, the system can independently learn and store each type of preference pattern, reducing the complexity of the overall learning model while improving the accuracy of user preference representation.
Solution Approach 2:
The system introduces dynamic modeling to capture transient setting changes that occur during vehicle operation. By representing settings as dynamic trajectories rather than static values, the system accurately records how users adjust settings over time in response to changing conditions, thereby improving user preference accuracy without excessive complexity increase.
3Device complexity
If transient setting changes are not considered, then data processing simplicity is improved, but setting recommendation quality deteriorates
Solution Approach 1:
The system performs preliminary extraction and storage of transient setting change patterns from historical data before they are needed for recommendations. By pre-processing and organizing transient change data in advance, the system reduces the complexity of real-time data processing while ensuring high-quality setting recommendations that account for both steady-state and transient user preferences.
Data Source
AI summary
An in-vehicle environment setting system 10 includes: a receiving unit 210 that receives, from a vehicle 50, setting information regarding a setting for an in-vehicle environment of the vehicle 50 and environment information regarding an outdoor environment of the vehicle 50 in a manner associated with an identity of a user 52 who boards the vehicle 50; a learning unit 220 that learns a preference of the user 52 related to the setting for the in-vehicle environment based on the setting information and the environment information; a generation unit 230 that generates recommended setting information regarding a recommended setting for the in-vehicle environment preferred by the user 52 based on a learning result of the preference; and a transmission unit 240 that transmits the recommended setting information to a vehicle 60 which is different from the vehicle 50.