Vehicle Cabin Occupancy Detection for Hidden Object Classification
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Solution Overview
Problem
Existing vehicle monitoring systems struggle to accurately detect and classify hidden objects in the cabin, such as rear-facing child seats or out-of-position passengers, due to limited field of view and high resource consumption, often requiring multiple sensors and complex installations.
Innovation Solution
A monitoring system that integrates multiple data inputs, including 2D and 3D imaging, RF sensing, and machine learning algorithms, to identify and track objects in real-time, using probabilistic models to update occupancy states continuously and reduce processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems with limited field of view are used, then device complexity is reduced, but measurement precision deteriorates due to inability to detect hidden objects
Solution Approach 1:
The patent transitions from 2D image data to 3D spatial representation by generating depth maps and 3D point clouds from multiple camera views. This dimensional transformation enables the system to detect hidden objects by reconstructing their spatial positions in three-dimensional space, overcoming the line-of-sight limitations of conventional 2D monitoring systems.
Solution Approach 2:
The patent introduces probabilistic occupancy maps as an intermediary representation that consolidates detection information from multiple sensors and time steps. This probabilistic model serves as a mediator that integrates data from different sources (multiple cameras, depth sensors, motion detectors) and temporal moments, enabling robust detection of hidden objects without requiring direct line-of-sight from each sensor.
2Measurement precision
If multiple sensors are deployed to improve detection coverage, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent designs a multi-functional sensor system where cameras serve multiple purposes: capturing 2D images for object recognition, generating depth maps for spatial understanding, and providing texture information for 3D reconstruction. This multi-functionality reduces the need for separate dedicated sensors for each function, thereby improving detection accuracy without proportionally increasing system complexity.
Solution Approach 2:
The patent merges data from multiple sensors and multiple processing streams into a unified probabilistic occupancy map. By combining information from different sensor types (imaging, depth, motion) and different processing approaches (2D detection, 3D reconstruction, temporal tracking) into a single integrated representation, the system achieves high detection accuracy while managing complexity through data fusion rather than parallel independent systems.
3Measurement precision
If complex processing algorithms are used to detect hidden objects, then measurement precision improves, but productivity deteriorates due to increased processing time
Solution Approach 1:
The patent performs preliminary processing by generating depth maps and 3D point clouds from camera images in advance of the main detection task. By pre-computing spatial representations and maintaining updated probabilistic occupancy maps that predict object locations, the system reduces the computational burden during real-time detection, enabling high accuracy hidden object detection without excessive processing delays.
Solution Approach 2:
The patent implements continuous tracking and updating of probabilistic occupancy maps that maintain detection information across multiple time steps. This continuity allows the system to carry forward detection results from previous frames, reducing the need for complete re-detection in each frame and enabling real-time performance while maintaining high accuracy for hidden objects through temporal integration.
Data Source
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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.