Vehicle Personalization Control Using Route-Based Predictive Adaptation
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Solution Overview
Problem
Current vehicle personalization systems fail to adapt promptly to a driver's needs due to reliance on past behavior data, leading to underutilization of driving mode benefits and decreased driver acceptance.
Innovation Solution
A method that anticipates a driver's needs by mapping dynamic parameters from other vehicles on the same route, incorporating feedback from the driver's current behavior, and using a proactive loop to adjust vehicle settings before the need arises, based on geolocation and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vehicle settings are adapted based on past behavior data, then personalization is achieved, but the adaptation comes too late to be appropriate for current driving needs
Solution Approach 1:
The system performs preliminary actions by predicting future driver needs based on current driving context and historical data, then pre-adapting vehicle settings before the actual need arises. This proactive approach eliminates the time delay inherent in reactive systems that only respond after observing past behavior patterns.
Solution Approach 2:
The system transitions from static, history-based adaptation to dynamic, context-aware prediction. By continuously analyzing current driving conditions, vehicle state, and historical patterns, the system dynamically adjusts settings in real-time, making the personalization both timely and adaptive to changing circumstances.
2Productivity
If driving modes are offered to drivers, then vehicle performance can be optimized, but drivers make little use of them due to lack of understanding
Solution Approach 1:
The system enables self-service by automatically selecting and applying optimal driving modes without requiring driver intervention. The vehicle's control system autonomously analyzes driving context, predicts needs, and adjusts performance parameters, eliminating the need for drivers to understand or manually select modes while still achieving performance optimization.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor driver behavior, vehicle state, and environmental conditions to automatically adjust settings. This closed-loop approach ensures optimal performance is maintained while removing the complexity of manual mode selection, as the system learns from and adapts to driver preferences over time.
3Measurement precision
If behavioral data from multiple vehicles is collected, then prediction accuracy improves, but data privacy and security concerns increase
Solution Approach 1:
The system applies local quality by processing and analyzing data locally within individual vehicle systems rather than centralizing all raw data. Each vehicle maintains its own behavioral patterns and predictions, sharing only anonymized aggregates or insights, thereby improving prediction accuracy through distributed intelligence while minimizing privacy risks through localized data handling.
Data Source
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AI summary
Method (1) for automatically adapting at least one personalization parameter of a motor vehicle, said method comprising: - A step of geolocating said motor vehicle; - A step of positioning (11) the motor vehicle on a road map according to said geolocation; - A step of acquiring (12) in a database at least one dynamic parameter of a plurality of other motor vehicles in the same position and/or in future positions of the motor vehicle on the road map; - A step of calculating said at least one personalization parameter adapted according to said at least one acquired dynamic parameter; and - A step of controlling the application of the personalization parameter adapted to the motor vehicle.