Smartphone Localization in Moving Vehicles Using Sensor Correlation
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Solution Overview
Problem
Existing methods for driver identification in vehicles require infrastructural modifications and significant installation costs, making them inefficient for accurately associating driving behavior with the correct driver, especially in scenarios with multiple passengers.
Innovation Solution
A method and system that utilize a smartphone's sensors to collect location coordinates, lane marking information, and acceleration patterns to determine the lateral, longitudinal, and vertical positions within a vehicle, correlating these data points to identify the driver's position and behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrastructure modifications are made in the vehicle to identify driver, then driver identification accuracy is improved, but installation cost and device complexity increase significantly
Solution Approach 1:
The smartphone serves itself by using its own built-in sensors (GPS, accelerometer, gyroscope, magnetometer) to determine its position within the vehicle. No external sensors or infrastructure modifications are needed - the device performs the localization function using its inherent capabilities.
Solution Approach 2:
Instead of installing dedicated identification infrastructure in the vehicle, the system uses data from the smartphone that the driver already carries. The smartphone's existing sensor data is copied and processed to create the identification system, avoiding the need for separate identification hardware.
2Reliability
If multiple sensors are installed in the vehicle to track driver behavior, then driver monitoring capability is improved, but installation cost and technical compatibility requirements increase
Solution Approach 1:
The smartphone performs multiple functions: it serves as the identification device, the sensor array for tracking motion, the positioning system, and the communication device. This single universal device replaces what would otherwise require multiple specialized sensors and systems.
Solution Approach 2:
The smartphone uses its own built-in sensors (accelerometer, gyroscope, magnetometer, GPS) to collect all necessary data for driver behavior monitoring. The device serves itself by leveraging its existing sensor suite rather than requiring additional sensors to be installed in the vehicle.
3Productivity
If additional communication devices are installed to send real-time data to insurance companies, then data transmission capability is improved, but device complexity and installation cost increase
Solution Approach 1:
The smartphone's existing communication capabilities (cellular, Wi-Fi, Bluetooth) are used for data transmission to insurance companies. The same device that collects the data also transmits it, eliminating the need for separate communication hardware.
Solution Approach 2:
The smartphone uses its own built-in communication modules to send the collected driving behavior data to insurance companies. The device performs both data collection and data transmission functions without requiring additional communication infrastructure.
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
Enables efficient and cost-effective driver identification by accurately localizing the smartphone within the vehicle, improving the integrity of driver monitoring data and providing insights into driving behavior for insurance premium determination.
Implementation Method 1
the vertical acceleration pattern associated with the at least front wheel and vertical acceleration pattern associated with the at least rear wheel computed from an accelerometer sensor present in the smartphone
Data Source
AI summary
A method and apparatus for localizing a smartphone is disclosed. A plurality of lateral position samples of the smartphone based at least in part on a plurality of location coordinates, lane marking information and accuracy factors of the plurality of location coordinates are collected at a plurality of time instances. Lateral position of the smartphone in the moving vehicle based at least on the plurality of lateral position samples collected at the plurality of time instances is determined. One or more correlations between a vertical acceleration pattern associated with at least one front wheel and a vertical acceleration pattern associated with at least one rear wheel of the moving vehicle passing over one or more road discontinuities as computed from an accelerometer sensor present in the smartphone are determined. Longitudinal position of the smartphone in the moving vehicle based at least on the one or more correlations is determined.