Digital Key Vehicle Control for AI Departure Preparation
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Solution Overview
Problem
Conventional digital key systems lack the ability to actively prepare a vehicle for departure based on user patterns and schedules, requiring manual intervention by the user.
Innovation Solution
A smart vehicle control device and method that utilizes reinforcement learning to generate an active vehicle control schedule through user input and vehicle interaction, incorporating supervised learning for static factors, and employs a deep Q network (DQN) to optimize vehicle control policies based on user daily patterns and real-time inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If digital key technology is used for vehicle control, then vehicle access and engine starting functions are provided, but the system cannot actively prepare the vehicle for departure based on user patterns
Solution Approach 1:
The system performs preliminary actions by proactively preparing the vehicle for departure before the user actually requests it. The AI model predicts user departure intentions based on patterns like location, time, and historical behavior, then automatically initiates vehicle preparation sequences including climate control, entertainment system setup, and other pre-configured functions, eliminating the need for users to manually prepare each system.
Solution Approach 2:
The vehicle system serves itself by using its own data and AI capabilities to automatically determine when and how to prepare for departure. The embedded AI model continuously learns from user interactions and vehicle operational data, enabling the system to autonomously optimize preparation schedules and adjust to changing user preferences without external intervention or complex user configuration.
2Ease of operation
If AI reinforcement learning is implemented to optimize vehicle control schedules, then user convenience is enhanced, but computational resources and processing time increase
Solution Approach 1:
The system applies partial reinforcement learning by focusing computational resources on the most impactful vehicle functions and departure scenarios. Rather than continuously optimizing all vehicle systems equally, the AI concentrates learning efforts on high-priority preparation tasks identified through initial pattern recognition, achieving significant user convenience improvements with moderate computational overhead.
Solution Approach 2:
The reinforcement learning model operates periodically rather than continuously, updating vehicle control schedules at strategically determined intervals based on detected pattern stability and user behavior changes. This periodic updating reduces computational energy consumption by avoiding redundant calculations when user patterns remain consistent, while still maintaining up-to-date optimization when meaningful changes occur.
3Adaptability or versatility
If the system continuously learns user patterns through reinforcement learning, then the vehicle control schedule becomes more personalized, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential and most informative features from user interaction data for reinforcement learning processing. Rather than continuously analyzing all raw data including redundant information, the AI model identifies and extracts key patterns such as departure time preferences, frequently used functions, and contextual triggers, reducing data processing requirements while maintaining high personalization accuracy.
Solution Approach 2:
The learning process is segmented into distinct phases: initial pattern detection, reinforcement learning optimization, and periodic refinement. Each phase processes data at appropriate granularity levels, with early stages using coarser pattern recognition and later stages applying more detailed analysis only when needed, thereby reducing overall data processing load while achieving comprehensive personalization.
Data Source
AI summary
Provided is a smart vehicle control device including: a user input receiver receiving a user input from a user terminal through a digital key application; a vehicle input receiver receiving a vehicle input input from a vehicle by a user boarding on the vehicle; a reinforcement learning model generating a vehicle control policy for vehicle driving preparation through reinforcement learning based on the user input and the vehicle input; and a vehicle controller configured for controlling the vehicle based on the vehicle control policy.