Ultrasonic Driver Identification via Relative Distance Sensing
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Solution Overview
Problem
Conventional systems struggle to accurately identify the driver in a vehicle with multiple occupants and fail to timely detect when a user approaches a crosswalk, leading to potential distractions and safety risks.
Innovation Solution
The use of ultrasonic sensing techniques with high-frequency transmitters to determine the relative distance of mobile devices to a signal transmitter, combined with machine learning classifiers that analyze ultrasonic and sensor data to identify the driver and detect crosswalk proximity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional geolocation systems are used to detect user location, then the system structure remains simple, but the system fails to timely detect when the user approaches a crosswalk
Solution Approach 1:
The system segments the detection task by using ultrasonic sensors mounted on the vehicle to detect crosswalks independently from mobile device geolocation. This separates the crosswalk detection function from the user's mobile device, allowing timely detection without requiring complex integration of multiple systems.
Solution Approach 2:
The ultrasonic sensing system acts as an intermediary between the vehicle and the user. It detects crosswalks in the vehicle's path and communicates this information to the user's mobile device, enabling timely warnings without directly modifying the user's device or requiring complex geolocation processing on the device itself.
2Measurement precision
If multiple mobile devices are present in the vehicle, then the system must handle more devices, but conventional systems cannot identify which occupant is the driver
Solution Approach 1:
The system replaces manual driver identification (mechanical approach of asking or assuming) with ultrasonic sensing technology. The ultrasonic sensors automatically detect the occupant in the driver's seat position and identify their mobile device, providing precise driver identification without requiring complex manual processes or additional hardware interfaces with each device.
Solution Approach 2:
The ultrasonic sensing system performs self-service by automatically identifying the driver and associating the correct mobile device without requiring user intervention. The system autonomously determines which occupant is the driver based on seat position detection and automatically links the corresponding mobile device, eliminating the need for manual device pairing or user input.
3Measurement precision
If ultrasonic sensing data and sensor data are combined with machine learning classifiers, then the driver identification accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The system merges ultrasonic sensing data with data from multiple sensor types (infrared, sound, pressure, motion sensors) to create a comprehensive data set for driver identification. This combination of multiple sensing modalities improves detection accuracy by cross-validating signals and reducing false positives, while the integrated approach manages complexity through unified processing architecture.
Solution Approach 2:
The machine learning classifier processes sensor data by transforming raw sensor readings into meaningful features and parameters. The system changes the parameter representation from raw sensor values to classified occupant roles and positions, enabling accurate driver identification while managing complexity through parameter transformation and feature extraction.
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 solution effectively identifies the driver in a vehicle and provides timely alerts for crosswalk proximity, reducing distractions and enhancing safety by accurately determining occupant roles and monitoring crosswalk approaches.
Implementation Method 1
a signal transmitter mounted inside or outside of a vehicle. The signal transmitter may include a high-frequency ultrasonic transmitter
Implementation Method 2
Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined
Data Source
AI summary
Aspects of the disclosure relate to using ultrasonic or other types of signals to determine a distance between a transmitter and one or more mobile devices. The distance may be used to facilitate travel on foot or in a vehicle. One aspect disclosed provides a computing platform that may receive ultrasonic sensing data associated with mobile devices in a vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.