In-Vehicle Occupant Monitoring With Multi-Sensor Anomaly Detection
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Solution Overview
Problem
Current safety systems fail to effectively monitor and prevent tragedies caused by drowsy or distracted driving and unattended occupants in vehicles, particularly children left in parked cars, with high false alarm rates and lack of direct monitoring capabilities.
Innovation Solution
An in-vehicle detection system utilizing a geophone vibration sensor, temperature sensor, CO2 sensor, and thermal imaging camera, along with AI/ML algorithms, to detect unattended occupants and unsafe driving conditions, employing multiple sensors for low false alarm rates and providing countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used to detect unattended occupants, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent sensor modules (geophone vibration sensor, temperature sensor, CO2 sensor, thermal imaging camera), each responsible for detecting specific parameters. This segmentation allows the system to improve detection reliability through multiple data sources while managing complexity by modularizing each sensor's function and processing pipeline.
Solution Approach 2:
The controller integrates multiple detection functions (vibration analysis, temperature monitoring, CO2 level detection, thermal imaging) into a single unified system that processes data from all sensors to determine unattended occupant presence. This multi-functionality approach consolidates complexity into a central control unit while maintaining high reliability through cross-validation of multiple sensor types.
2Measurement precision
If AI/ML algorithms are implemented for analysis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Traditional rule-based detection algorithms are replaced with AI/ML-based analysis systems that automatically learn patterns from sensor data. The controller employs machine learning models to analyze vibration patterns, temperature trends, and thermal images, significantly improving detection precision by identifying subtle indicators of unattended occupants that rule-based systems would miss, while the computational complexity is managed through efficient model deployment.
3Reliability
If continuous monitoring is implemented, then safety coverage is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data at optimized intervals rather than truly continuous monitoring. The controller adjusts sampling frequencies based on vehicle state (parked vs. moving) and environmental conditions, maintaining comprehensive safety coverage during critical periods while reducing energy consumption during low-risk periods through intelligent duty cycling of sensors and processing.
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 reliable detection of unattended occupants and unsafe driving behaviors with low false alarm rates, reducing the risk of heat-related injuries and fatalities, and offering timely interventions.
Implementation Method 1
a geophone vibration sensor configured to continuously sample at a predetermined sampling rate the vibration produced by the vehicle
Implementation Method 2
a cabin temperature sensor configured to continuously sample at a predetermined sampling rate the temperature within the vehicle
Implementation Method 3
a thermal imaging camera configured to monitor the rear cabin of the vehicle
Implementation Method 4
a cabin carbon dioxide (CO2) sensor configured to continuously sample at a predetermined sampling rate the CO2 levels within the vehicle
Data Source
AI summary
In-vehicle occupant detection system and methods capable of monitoring the interior of a passenger vehicle and responding to potentially dangerous situations are disclosed. The detection systems may comprise a front camera to focus on driver and passenger behavior in the front cabin, and a rear camera and an array of environmental sensors to detect unattended occupants including children, adults or pets in the rear seats. Data may be processed by an intelligent algorithm to assesses anomalies and trigger one or more alarms or countermeasures.