Sensor Fusion Phone Localization for In-Vehicle Mode Classification
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Solution Overview
Problem
Existing systems for locating and classifying the operational mode of mobile communication devices within a defined volume, such as vehicles, lack real-time calibration and maintenance detection, which are crucial for ensuring driving safety.
Innovation Solution
A sensor fusion approach is introduced to classify the operational mode, interaction events, and calibration/maintenance events of mobile communication devices using a combination of RF signals, inertial and vital signs sensors, and computer processors to determine the device's position and interaction type, enabling real-time calibration and maintenance detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor fusion approach is implemented to classify operational modes and detect calibration/maintenance events in real-time, then driving safety and system reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the classification task into multiple independent classifiers, each responsible for specific operational modes or event types. This modular approach allows the complex sensor fusion problem to be divided into manageable classification modules that can be processed independently, reducing overall system complexity while maintaining high reliability through comprehensive coverage of different operational scenarios
Solution Approach 2:
The patent implements a universal classification framework that handles multiple types of events (operational modes, calibration events, maintenance events) using a single integrated sensor fusion system. This multi-functional approach consolidates what would otherwise require separate detection systems, improving reliability without proportionally increasing complexity
2Measurement precision
If multiple sensors are integrated to determine device position and interaction type, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges data from multiple sensors (accelerometers, gyroscopes, magnetometers, barometers, GPS) into a unified position determination system. By combining these sensors into a single integrated framework that processes their outputs collectively, the system achieves high measurement precision while avoiding the complexity of managing each sensor independently with separate processing chains
3Productivity
If real-time classification of operational modes is performed using sensor fusion, then responsiveness and productivity are improved, but use of energy increases
Solution Approach 1:
The system employs periodic classification operations rather than continuous processing, performing sensor fusion and operational mode classification at optimized intervals. This periodic approach maintains real-time responsiveness for safety-critical decisions while reducing overall energy consumption by avoiding unnecessary continuous computation during stable operational states
Solution Approach 2:
The classification frequency and computational intensity are dynamically adjusted based on operational context. During critical transitions or when safety concerns are detected, the system increases processing intensity for rapid classification. During stable periods, it reduces computational activity, optimizing the balance between productivity and energy consumption
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 accurate classification of device modes and events, ensuring reliable operation and safety by providing real-time recalibration and maintenance alerts, enhancing driving safety.
Implementation Method 1
determining a position of the at least one mobile communication device relative to a frame of reference of the defined volume, based on at least one of: angle or phase of arrival, time of flight such as Round Trip Time (RTT) and Time Difference Of Arrival (TDOA)
Implementation Method 2
received intensity of radio frequency (RF) signals transmitted by the at least one mobile communication device and received by the PLU
Data Source
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AI summary
A method and a system for classifying an operational mode of one of a plurality of communication devices forming a system, or an event related to communication between the communication devices, within a defined volume, are provided herein. The communication devices may include a phone location unit (PLU) and one or more mobile communication devices, wherein inside the defined volume there is a person interacting with one or more mobile communication devices. The method may include: determining a position of the mobile communication device using the PLU; obtaining sensor measurement related to the one mobile communication device, from a sensor located within or outside the defined volume; and using a computer processor to classify the operational mode of the one of the communication devices or an event related to communication therebetween, into one of a plurality of predefined operational modes or events, based on the position and the sensor measurement.