Vehicle Control System for Time-Based Driver Learning
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Solution Overview
Problem
Luxury vehicles do not provide a personalized driving experience tailored to each driver's unique preferences and habits, as existing systems primarily rely on manual adjustments and memorization of settings without adaptive automation.
Innovation Solution
A system that identifies specific drivers and learns their repetitive behaviors, automatically adjusting vehicle settings such as audio, HVAC, seat, steering, and navigation systems based on time and day of the week, allowing for personalized and automated settings without driver interaction once consistent preferences are established.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual adjustments and memorization of settings are used, then drivers can customize their preferences, but the system lacks adaptive automation and requires repeated manual intervention
Solution Approach 1:
The system automatically monitors driver adjustments and learns preferences without requiring manual input or programming. The vehicle system serves itself by observing and adapting to driver behavior patterns, eliminating the need for drivers to manually program settings while providing automated customization.
Solution Approach 2:
The system continuously monitors driver adjustments to vehicle settings and uses this feedback to learn and adapt preferences. By observing repeated adjustment patterns, the system refines its understanding of driver preferences and automatically applies learned settings, creating a closed-loop learning system.
2Adaptability or versatility
If existing memory systems preset and memorize driver settings, then vehicle characteristics can be adjusted, but the system does not learn repetitive driver behaviors or provide time-sensitive customization
Solution Approach 1:
The system proactively adjusts vehicle settings based on learned driver preferences and temporal patterns before the driver needs them. By predicting when adjustments will be needed based on time of day, day of week, and historical behavior patterns, the system performs preliminary customization automatically.
Solution Approach 2:
The system dynamically adapts vehicle settings based on real-time conditions including time of day, day of week, and observed driver behavior patterns. Rather than static memorized presets, the system continuously evolves its understanding of driver preferences and adjusts settings dynamically according to contextual factors.
3Ease of operation
If drivers manually adjust settings each time, then precise control is maintained, but convenience and comfort are reduced due to repeated intervention
Solution Approach 1:
The vehicle system automatically monitors and learns driver adjustment patterns, then self-adjusts settings without requiring driver intervention. This eliminates the manual operation burden while maintaining precise control according to learned preferences, significantly improving convenience and reducing time loss.
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 at approximately the same time of day on the same day of the week. When the control system determines that the identified driver repeats the same behavior in response to the same temporal conditions, the controller learns that behavior and associates it with the identified driver so that it can be automatically performed, without driver interaction, under the same temporal conditions in the future.