Location-Sensitive Driver Learning Interface
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Luxury vehicles do not provide a personalized driving experience tailored to each driver's unique preferences and driving style, despite features like customizable driving modes and seat settings, as they lack the ability to automatically adjust settings based on individual driver habits and locations.
Innovation Solution
A system that identifies specific drivers using various methods, monitors user-settable vehicle functions, and automatically adjusts settings when the driver is within a preset distance of a specific location, learning and replicating settings if consistently adjusted by that driver, with options for driver confirmation and default settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automatic seat positioning and driver-selectable driving modes are provided, then user customization is improved, but the system cannot automatically adapt to individual driver habits and locations
Solution Approach 1:
The system monitors driver adjustments to vehicle functions over time and automatically learns preferred settings without requiring manual programming. The controller detects when a driver manually adjusts a function (e.g., climate controls, audio, seat position) and automatically sets that function to the observed preference, enabling the system to serve itself by learning from driver behavior patterns.
Solution Approach 2:
The system performs preliminary monitoring of driver adjustments during a learning period before automatically applying learned preferences. The controller collects data on driver adjustments over a predetermined period, establishes baseline preferences in advance, and then proactively applies these settings automatically once the learning phase is complete, preparing the system ahead of time for personalized service.
2Adaptability or versatility
If the system monitors and learns driver adjustments over time, then personalized settings are achieved, but system complexity increases
Solution Approach 1:
The existing vehicle controller is enhanced to perform multiple functions: it continues to manage standard vehicle operations while simultaneously monitoring driver adjustments, learning preferences, and automatically applying personalized settings. This multi-functionality approach allows the system to gain personalized capabilities without adding separate dedicated hardware systems, thereby reducing overall complexity.
Solution Approach 2:
The system implements a feedback loop where the controller continuously monitors driver adjustments to vehicle functions, compares observed behavior against learned preferences, and automatically corrects settings to match driver preferences. This closed-loop feedback mechanism enables automatic adaptation using existing control infrastructure, avoiding the need for complex separate monitoring and actuation systems.
3Ease of operation
If the system automatically modifies vehicle functions based on location, then driver convenience is improved, but energy consumption increases
Solution Approach 1:
The system monitors driver adjustments periodically over a predetermined time period rather than continuously, and only activates automatic modification after a threshold number of consistent adjustments are observed. This periodic monitoring approach reduces energy consumption compared to continuous monitoring while still capturing driver behavior patterns effectively, balancing convenience with energy efficiency.
Data Source
AI summary
A vehicle control system is provided that is able to (i) identify a particular driver from other potential drivers and (ii) monitor various vehicle functions in order to determine if the identified driver repeatedly performs the same behavior when the vehicle is at or near the same location. When the control system determines that the identified driver repeats the same behavior in response to the vehicle being at or near the same location, the controller learns that behavior and associates it with the identified driver so that it can be automatically performed, without driver interaction, when the vehicle is at or near the same location in the future.