Vehicle Cabin Occupancy Detection for Hidden Passenger 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 passengers or child seats, due to limited field of view and resource-intensive processing, often requiring multiple sensors and complex installations, which are costly and inefficient.
Innovation Solution
A method and system using a monitoring system with multiple sensing devices and processors to analyze images and additional data, employing deep neural networks and Markov matrices to predict occupancy states, integrating data from various vehicle systems for real-time updates and device control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems use sensors mounted in the vehicle requiring line of sight with imaged targets, then the system structure is simple, but the detection accuracy deteriorates for hidden objects blocked by vehicle seats or headrests
Solution Approach 1:
The patent transitions from 2D image-based detection to 3D point cloud data acquisition using LiDAR technology. This dimensional change enables the system to detect hidden objects by capturing spatial information from multiple angles and depths, overcoming occlusions caused by vehicle seats and headrests that block conventional 2D camera views.
Solution Approach 2:
The patent introduces a point cloud processing module as an intermediary between raw sensor data and object detection. This module transforms complex multi-source sensor inputs into structured point cloud representations, enabling more accurate detection of hidden objects while managing system complexity through systematic data processing.
2Reliability
If monitoring systems are installed in the front part of the car above or below the cabin front mirror, then the installation is simple, but the field of view is limited resulting in low reliability for detecting hidden areas
Solution Approach 1:
The patent divides the vehicle interior monitoring into multiple detection zones using distributed sensor placement. Instead of relying on a single front-mounted camera, the system uses multiple LiDAR sensors positioned at different locations, each responsible for specific zones, thereby improving coverage of hidden areas while maintaining manageable system complexity through modular deployment.
Solution Approach 2:
The patent designs the monitoring system to perform multiple functions: detecting occupants, classifying objects, determining positions, and identifying hidden areas. This multi-functional approach increases reliability by providing comprehensive monitoring capabilities from a unified system rather than requiring separate specialized sensors for each function.
3Measurement precision
If prior monitoring systems use multiple sensors embedded in various locations in the vehicle cabin, then the detection coverage is improved, but the system becomes expansive, complicated, and difficult to install
Solution Approach 1:
The patent combines multiple sensor types (cameras, LiDAR, radar) into an integrated multi-sensor monitoring system with a unified processing architecture. This merging approach maintains comprehensive detection coverage by utilizing complementary sensor modalities while reducing installation complexity through centralized control and coordinated data processing rather than independent sensor systems.
Solution Approach 2:
The patent implements dynamic sensor activation and data processing based on detected conditions. The system adaptively adjusts which sensors are active and how data is processed based on occupancy patterns and detection needs, maintaining high detection coverage while reducing computational burden and installation complexity by avoiding continuous full-system operation.
4Measurement precision
If conventional monitoring methods require significant processing resources and long training time, then the detection accuracy can be maintained, but resource consumption increases and processing time is extended
Solution Approach 1:
The patent performs preliminary processing of sensor data into structured point cloud representations before main object detection and classification. This pre-processing step organizes raw data into meaningful spatial structures, reducing the computational burden on subsequent detection algorithms and improving processing efficiency while maintaining detection accuracy through preserved spatial relationships.
Solution Approach 2:
The patent replaces traditional mechanical image processing methods with point cloud-based computational geometry approaches. This substitution enables more efficient processing of spatial data by utilizing mathematical operations on point cloud structures rather than complex image analysis, improving productivity while maintaining or enhancing detection precision.
Data Source
AI summary
System and methods are provided for detecting occupancy state in a vehicle including analyzing data of the interior passenger compartment to yield at least one probability of a current occupancy state of the vehicle and further analyzing additional data of the interior passenger compartment to yield predicted probabilities of the occupancy state of the vehicle, wherein each probability of the predicted probabilities relate to the probability of changing the 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 the vehicle; determining the current occupancy state based on the updated probability of an updated occupancy state of the vehicle and generating an output to control one or more devices or applications based on the determined occupancy state.