In-cabin Occupant Detection Using Motion Pattern Analysis
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Solution Overview
Problem
Current systems fail to accurately differentiate between animate and inanimate objects within a vehicle's cabin during motion, which is crucial for various vehicle operations and occupant monitoring.
Innovation Solution
A method and system that utilize sensors and machine learning algorithms to detect movement patterns, distinguishing between animate and inanimate objects based on vertical and horizontal distance analysis while the vehicle is in motion, employing sensors like Millimeter Wave Radar and processing units to classify objects as occupants or inanimate items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current object detection systems are used in vehicles, then basic object detection is achieved, but accurate differentiation between animate and inanimate objects during vehicle motion is not achieved
Solution Approach 1:
The system dynamically adapts detection parameters and processing methods based on vehicle motion state. During vehicle motion, the system adjusts sensitivity thresholds and activates motion-compensation algorithms to maintain reliable differentiation between animate and inanimate objects despite the dynamic environment.
Solution Approach 2:
The system changes detection parameters such as sensitivity thresholds, integration times, and processing filters based on vehicle motion detection. When motion is detected, parameters are adjusted to compensate for motion-induced artifacts, thereby improving both measurement precision and reliability of object classification.
2Measurement precision
If simple object detection is implemented, then system complexity is reduced, but the ability to distinguish between occupants and inanimate objects is insufficient
Solution Approach 1:
The detection system is segmented into multiple specialized modules: motion detection module, pattern recognition module, classification module, and control module. Each module performs a specific function in the detection pipeline, allowing complex differentiation tasks to be distributed across simpler, specialized components.
Solution Approach 2:
The system introduces intermediate processing stages including motion compensation algorithms and pattern analysis layers between the basic sensor input and final classification output. These intermediaries transform raw detection data into refined features that enable accurate object type differentiation without requiring overly complex direct detection methods.
3Adaptability or versatility
If motion-based detection is used during vehicle operation, then object detection coverage is improved, but false differentiation between moving objects and occupants occurs
Solution Approach 1:
The system applies motion detection thresholds and processing filters that are deliberately set to accommodate the full range of vehicle motions without being overly sensitive. By using partial motion detection (focusing on significant motions rather than all movements), the system maintains versatility during vehicle operation while avoiding false positives from minor vibrations or movements.
Solution Approach 2:
The system continuously monitors detection results and adjusts its sensitivity and classification criteria based on feedback from multiple sensors and the known vehicle motion state. This feedback loop allows the system to maintain high detection coverage during motion while correcting for false differentiations through real-time parameter adjustment and verification.
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
Effectively identifies and classifies objects within the vehicle, enabling improved occupant monitoring and vehicle operation by accurately distinguishing between animate and inanimate objects through precise movement analysis.
Implementation Method 1
employing sensors like Millimeter Wave Radar and processing units to classify objects
Data Source
AI summary
An example operation includes one or more of detecting a movement of an object in a vehicle when the vehicle is in motion, and determining that the object is an inanimate object, based on a vertical and horizontal distance of the detected movement.