Smart Car Proximity Detection Using RF Signal Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smart car systems face challenges in accurately determining driver proximity due to variations in radio frequency signals from smart keys, especially when users have different usage patterns, making it difficult to collect personalized learning data without additional equipment or inconvenience to users.
Innovation Solution
A method and apparatus that involve a user device, vehicle device, and management device for receiving and transmitting signals, measurement data, and updated mapping information to improve proximity determination by detecting errors and updating machine learning models based on user feedback and data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional proximity determination methods are used based on signal strength, then the system can operate with simple hardware, but accuracy deteriorates due to signal variations from different usage patterns
Solution Approach 1:
The system implements feedback by detecting errors in proximity determination results and using these errors to update mapping information. The vehicle device receives error information when the determined proximity does not match the actual proximity, and uses this feedback to refine the mapping between measurement data and proximity states, thereby improving accuracy over time.
Solution Approach 2:
The system performs preliminary actions by collecting measurement data and building mapping information in advance through normal operation. The vehicle device accumulates measurement data from signal transmissions and pre-processes it to create mapping information that can be used for future proximity determinations, preparing the system for accurate operation before actual use cases occur.
2Measurement precision
If personalized learning data is collected for each user, then proximity determination accuracy improves, but user convenience deteriorates due to additional equipment requirements
Solution Approach 1:
The system implements self-service by automatically collecting measurement data and generating personalized mapping information without requiring user intervention. The vehicle device autonomously performs data collection, error detection, and mapping information updates, eliminating the need for users to carry additional equipment or manually provide training data while still achieving personalized accuracy.
Solution Approach 2:
The system achieves universality by using a single existing user device (smartphone or smart key) for multiple purposes: both as the source of RF signals for proximity measurement and as the carrier of personalized mapping information. This eliminates the need for separate equipment for data collection and maintains user convenience while enabling personalized accuracy.
3Measurement precision
If mapping information is updated frequently to improve accuracy, then proximity determination precision improves, but information loss increases due to error detection requirements
Solution Approach 1:
The system converts the potentially harmful loss of information into a benefit by using detected errors as valuable training data. When proximity determination errors occur, instead of discarding this information, the system captures and analyzes the error cases to update and improve the mapping information, transforming information loss into opportunities for learning and improvement.
Data Source
AI summary
The present disclosure relates to determining proximity in a smart car system, and a method for operating a vehicle system comprises the steps of: receiving at least one signal transmitted by a user apparatus; transmitting measurement data for the at least one signal to a management apparatus; and receiving updated mapping data from the management apparatus for the measurement data and proximity data.