Vehicle Cabin Occupancy Detection Using Rear-View Sensor Fusion
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Solution Overview
Problem
Current vehicle monitoring systems struggle to accurately detect and classify objects, especially hidden ones, in the cabin due to limited field of view and reliance on line-of-sight sensors, leading to incomplete occupancy information and resource-intensive processing.
Innovation Solution
A method and system using a processor to analyze data from multiple sources, including images and sensor data, to determine the occupancy state of a vehicle cabin by combining current and predicted probabilities through a Markov matrix, utilizing deep neural networks and Infinite Impulse Response filters, to provide accurate and efficient object detection and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If sensors are positioned in the front part of the vehicle (e.g., above or below the cabin front mirror), then the monitoring system can be installed with a simple structure, but the field of view is limited and hidden areas in the cabin cannot be monitored
Solution Approach 1:
The patent positions sensors in the rear part of the vehicle cabin (e.g., above the rear window or on the rear pillar) rather than in the front, fundamentally changing the spatial dimension of sensor placement. This rear-positioned perspective enables the sensors to capture a different field of view that includes hidden areas such as the back seats and regions blocked by front seats, thereby resolving the contradiction between limited field of view and simple installation structure.
2Reliability
If multiple sensors are embedded in various locations in the vehicle cabin to monitor hidden areas, then the field of view and monitoring coverage are improved, but the system becomes expensive, complicated, and difficult to install
Solution Approach 1:
The patent makes a single rear-positioned sensor perform multiple functions: it monitors the rear seats, detects passengers in hidden areas, tracks passenger movements, and provides comprehensive occupancy information. This multi-functional approach eliminates the need for multiple specialized sensors distributed throughout the cabin, thereby maintaining high detection accuracy while significantly reducing system complexity and installation difficulty.
Solution Approach 2:
The patent combines the functions of multiple potential sensors into a single rear-positioned monitoring system. Instead of having separate sensors for different cabin zones, the invention merges all detection capabilities into one strategically positioned unit that leverages the rear perspective to achieve comprehensive coverage, thus reducing the number of components and simplifying the overall system architecture.
3Reliability
If conventional monitoring systems with limited field of view are used, then the device complexity is low, but the reliability of detection is reduced due to inability to view hidden areas
Solution Approach 1:
The patent changes the monitoring perspective from the conventional front-positioned view to a rear-positioned view. This dimensional change in sensor placement allows the system to see around and behind obstacles that block front-mounted sensors, thereby improving detection reliability for hidden areas without significantly increasing system configuration complexity.
4Measurement precision
If processing resources are increased to improve detection accuracy and handle complex scenarios, then the measurement precision is improved, but the processing time and resource dissipation increase
Solution Approach 1:
The patent performs preliminary action by capturing images at multiple predetermined positions (front, rear, left, right) in advance, then uses image fusion algorithms to combine these pre-captured images into a comprehensive view. This approach allows the system to achieve high detection accuracy for complex scenarios such as hidden passengers and non-standard positions without requiring excessive real-time processing resources, as the heavy lifting of data collection is done in advance.
Data Source
AI summary
System and methods are provided for detecting occupancy state in a vehicle having an interior passenger compartment the systems and methods include analyzing data of the interior passenger compartment to yield at least one probability of a current occupancy state of said vehicle and further analyzing additional data of the interior passenger compartment to yield predicted probabilities of said occupancy state of said vehicle, wherein each probability of the predicted probabilities relate to the probability of changing said current occupancy state to different occupancy state combining the current at least one probability of the current occupancy state with the predicted probabilities of the occupancy state to yield an updated probability of an updated occupancy state of said vehicle; determining the current occupancy state based on said updated probability of an updated occupancy state of said vehicle and generating an output to control one or more devices or applications based on the determined occupancy state.