Vehicle Access Control via Machine Learning Pattern Recognition
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Solution Overview
Problem
Current vehicle access and power management systems lack efficient methods to determine the environment of identification devices and vehicles, leading to unnecessary power consumption and potential security vulnerabilities.
Innovation Solution
The system utilizes machine learning algorithms programmed into the ECU system to receive data from environment sensors, identify patterns, and adjust access permissions and power conditions based on comparisons of vehicle and identification device environments, altering search and listen patterns and power states accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle continuously monitors environment sensors and maintains active communication with identification devices, then security and access control are improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the search and listen pattern based on environmental conditions and learned patterns. The ECU system modifies communication frequency and sensor monitoring intensity in real-time, transitioning between active monitoring and power-saving states while maintaining security through adaptive pattern recognition
Solution Approach 2:
The vehicle employs periodic search and listen patterns instead of continuous monitoring. The ECU system schedules communication attempts and sensor readings at intervals, reducing power consumption while maintaining security through regularly spaced detection cycles that can be adjusted based on environmental context
2Reliability
If the vehicle uses detailed environment sensor data and complex pattern recognition, then access security is improved, but device complexity increases
Solution Approach 1:
The ECU system performs self-learning by automatically analyzing environment sensor data and identifying patterns without requiring manual programming of specific environmental conditions. The system serves itself by continuously improving its access control decisions through machine learning algorithms that adapt to observed patterns in sensor data
Solution Approach 2:
The patent replaces traditional rule-based access control systems with machine learning-based pattern recognition. Instead of manually configuring security rules for specific environmental conditions, the system uses algorithms to automatically learn and recognize patterns from sensor data, substituting mechanical configuration with intelligent computation
3Use of energy by stationary object
If the vehicle reduces power consumption by limiting sensor monitoring and communication, then power management is improved, but security vulnerabilities increase
Solution Approach 1:
The system performs preliminary learning during periods when security requirements are lower, storing recognized patterns and environmental profiles in the ECU system. This preliminary action allows the vehicle to make faster security decisions during critical moments without requiring intensive real-time processing, thus balancing power management with security
Data Source
AI summary
A method for access to a vehicle includes receiving data from an identification device related to at least one device environment sensor of the identification device. At least one pattern associated with the received data is identified. An environment of the identification device based on feedback from the at least one device environment sensor is determined. The environment to the at least one pattern is compared. In response to the comparing step, access to the vehicle is allowed or denied.