Smartphone Sensor Fusion for Vehicle Occupant Side Classification
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Solution Overview
Problem
Current occupant detection systems in vehicles face challenges in reliably identifying and classifying occupants, particularly distinguishing between drivers and passengers, especially when smartphones are not connected to the vehicle's digital systems, which limits accurate biunique identification and classification.
Innovation Solution
A smartphone-based system utilizing a combination of accelerometer and gyroscope sensors to detect movement patterns and rotation directions during vehicle entry and exit, employing machine learning algorithms for decision-tree classification to determine whether the user is a driver or passenger, independent of phone position and orientation, and resistant to low-quality sensor data and variations in entry behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If smartphone sensors (accelerometer and gyroscope) are used to detect movement patterns during vehicle entry and exit, then occupant classification accuracy is improved, but the system complexity increases due to the need for machine learning algorithms and multi-sensor integration
Solution Approach 1:
The patent applies multi-functionality by using a smartphone that already contains multiple sensors (accelerometer, gyroscope, GPS) for various purposes, and repurposing these sensors for occupant classification. The same device serves both as a communication tool and as an occupant detection system, eliminating the need for dedicated in-vehicle hardware while maintaining high classification accuracy through machine learning algorithms.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary that processes raw sensor data from the accelerometer and gyroscope, transforming complex multi-sensor inputs into accurate occupant classification decisions. This intermediary layer handles the complexity of sensor integration and pattern recognition, allowing the system to achieve high measurement precision without requiring direct complex hardware integration.
2Reliability
If dedicated in-vehicle hardware is used for occupant detection, then identification reliability is improved, but the cost and device complexity increase
Solution Approach 1:
The patent applies self-service by enabling the smartphone to perform occupant classification functions independently without requiring dedicated in-vehicle hardware. The smartphone's existing sensors and processing capabilities are sufficient to detect movement patterns, determine entry/exit sides, and classify occupants reliably, making the system self-sufficient and eliminating the need for additional vehicle-mounted detection devices.
Solution Approach 2:
The patent effectively copies the functionality of dedicated in-vehicle occupant detection systems by using the smartphone's sensors to replicate what would otherwise require specialized hardware. The machine learning model processes smartphone sensor data in the same way that dedicated systems would process their sensor inputs, achieving comparable reliability without the need for physical copying or integration of actual vehicle hardware.
3Measurement precision
If multiple sensors are integrated to detect movement patterns and rotation directions, then classification accuracy is improved, but the ease of operation decreases due to the need for sophisticated algorithms
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with extensive sensor data collected during various vehicle entry and exit scenarios. The system performs preliminary data collection and model training offline, so that during actual operation, the pre-trained model can automatically classify occupants without requiring real-time complex computations. This preliminary preparation simplifies the operational process while maintaining high classification accuracy.
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
The system provides accurate and generic occupant classification, enabling personalized vehicle settings, risk-based insurance models, and enhanced safety measures by reliably identifying occupants without requiring dedicated in-vehicle hardware, improving the accuracy of driver and passenger detection.
Implementation Method 1
the mobile device measures gravitational acceleration movement sensory data by means of the accelerometer
Implementation Method 2
the plurality of sensors at least comprise an accelerometer and a gyroscope
Data Source
AI summary
A method and system for identifying and/or classifying a user of a vehicle based on sensory data measured by sensors of a cellular mobile device, the sensors at least comprising an accelerometer and a gyroscope. The mobile device measures gravitational acceleration movement sensory data by the accelerometer based on measuring parameters obtained from the accelerometer, vehicle entering or exiting movement patterns of the user being detected from the acceleration movement sensory data at least comprising pattern for base axis and degree of rotation associated with a vehicle entrance or exit of the user. The detected vehicle entering or exiting movement patterns of the user trigger as input features the recognition of a vehicle entering or exiting movement of the user by performing a decision-tree classification on the input features to rule out whether the user entered or exited from a left or right side of the vehicle.