Vehicle Passive Entry Distance Modeling with Context-Aware BLE Ranging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for passive vehicle entry using a mobile device face challenges in accurately estimating distance due to variations in mobile device types and environmental factors, leading to unreliable entry features.
Innovation Solution
The system improves distance estimation by using contextual information from the mobile device, such as its location and user activity, in conjunction with device profile information and signal strength, to select the most accurate distance model for passive entry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If distance estimation is based on RSSI from Bluetooth signals, then passive entry can be automatically initiated, but the distance estimation accuracy deteriorates due to variations in mobile device types and environmental factors
Solution Approach 1:
The system changes the parameters used for distance estimation from relying solely on RSSI to incorporating multiple parameters including contextual information (user activity, location, device orientation) and device profile information. This multi-parameter approach compensates for RSSI variations caused by different device types and environmental factors, improving distance estimation accuracy while maintaining automatic passive entry initiation.
Solution Approach 2:
The patent introduces contextual information and device profile information as intermediary data that mediates between the RSSI signal and the final distance estimation. These intermediaries provide additional context about the mobile device's state and environment, allowing the system to adjust distance calculations to account for signal variations without requiring manual intervention.
2Measurement precision
If multiple distance models are used to account for different environmental conditions, then distance estimation accuracy improves, but system complexity increases
Solution Approach 1:
The system implements dynamic selection of distance models based on real-time contextual information and device profile data. Instead of using a fixed complex model for all scenarios, the system dynamically chooses the most appropriate distance model for the current situation (e.g., pocket mode, hand-held mode, different environmental conditions). This dynamic approach maintains high accuracy across varying conditions while reducing the effective complexity by only activating necessary model components.
Solution Approach 2:
The patent applies different distance models and calculation methods tailored to specific local conditions such as device orientation, user activity, and environmental context. Each distance model is optimized for particular scenarios (e.g., one model for when the device is in a pocket, another for when held in hand), ensuring high accuracy for each local condition without requiring a single overly complex universal model.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and robustness of passive entry by accounting for various environmental conditions and device configurations, ensuring reliable vehicle unlocking based on precise distance estimation.
Implementation Method 1
Based on the received signal strength of the BLE signal (e.g., received signal strength indicator (RSSI)), the vehicle may estimate the distance between an approaching mobile device and the vehicle in real time
Data Source
AI summary
Systems and methods are provided for providing passive entry to a vehicle to a user with an authorized mobile device. Responsive to receiving, at a vehicle and from an application executing on a mobile device associated with a user, one or more signals associated with the mobile device, a signal strength and contextual information associated with the mobile device is determined based on the one or more signals. A passive entry feature of the vehicle is initiated based on the signal strength and the contextual information.