In-Vehicle Environmental Context Control With Reinforcement Learning
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Solution Overview
Problem
The interaction between users and vehicles for controlling environmental settings like climate, temperature, and lighting can distract drivers, deteriorating the user experience and safety while driving.
Innovation Solution
A novel reinforcement learning algorithm framework that learns user driving behavior to minimize user-vehicle interaction by determining environmental context and choosing actions that improve the driving experience, using machine learning models to sample rewards based on user reactions and adjust parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If hardware buttons, knobs, and virtual buttons are provided for controlling vehicle settings, then the user can control and change environmental context, but user attention is distracted when driving and user experience deteriorates
Solution Approach 1:
The system enables self-service by automatically adjusting vehicle environmental settings (temperature, lighting, climate) based on sensor data and machine learning predictions, eliminating the need for manual user interaction with buttons, knobs, or touchscreen interfaces during driving
Solution Approach 2:
The patent replaces mechanical control systems (physical buttons, knobs) and manual virtual interface interactions with an automated machine learning-based control system that uses sensors and algorithms to predict and adjust environmental settings without user input
2Adaptability or versatility
If multiple control options are provided for environmental settings, then the user can achieve better driving conditions, but the user needs to take a sequence of corresponding actions during driving
Solution Approach 1:
The system performs preliminary actions by pre-calculating and automatically applying optimal environmental settings based on predicted user needs, sensor data, and learned behavior patterns, eliminating the time users would otherwise spend making sequential adjustments
Solution Approach 2:
The patent dynamically changes environmental parameters (temperature, lighting intensity, climate settings) automatically based on sensor inputs and machine learning predictions, providing adaptability without requiring user time for manual adjustments
3Reliability
If the system learns user behavior through reinforcement learning, then user-vehicle interaction is minimized and driving safety is improved, but the system requires processing and analyzing user reactions
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user reactions to automatically adjusted settings and using this feedback to refine its predictions and adjustments, improving safety while managing complexity through iterative learning
Data Source
AI summary
Embodiments relate to a method, computer program and system for improving a user experience. The computer-implemented method for improving a user experience inside a vehicle comprises determining an environmental context inside the vehicle, wherein the environmental context is assigned a plurality of actions, which influences the environmental context. Further, the method comprises choosing at least one action of the plurality of actions for improving the user experience.