Vision-Based Airbag Enablement for Child Occupant Suppression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automotive supplemental passenger restraint systems, such as airbags, lack effective methods to suppress deployment for young child passengers or when a rear-facing child restraint system is present, leading to potential risks.
Innovation Solution
A system utilizing a camera to provide a two-dimensional image of the seating area, a seat weight sensor, and a controller with machine learning models for passenger detection and attribute determination, including height and weight analysis, to enable or disable airbag deployment based on passenger attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If airbag deployment is enabled for all passengers, then safety coverage is maximized, but risk of injury to young children increases
Solution Approach 1:
The system changes the parameter of airbag deployment from a binary on/off state to a conditional state based on detected passenger attributes. By measuring height, weight, and seating position parameters, the system determines whether deployment should be enabled or suppressed, thus protecting children while maintaining safety for adults.
Solution Approach 2:
The patent replaces manual lock-out mechanisms and simple weight sensing with a vision-based detection system using cameras and machine learning algorithms. This optical system substitutes mechanical approaches to achieve more accurate passenger classification and airbag control.
2Object-affected harmful factors
If manual lock-outs and weight sensing are used to suppress airbag deployment, then child safety is improved, but measurement precision and reliability decrease
Solution Approach 1:
The vision-based system performs multiple functions simultaneously: detecting passenger presence, determining height, estimating weight, and assessing seating position. This multi-functional approach replaces separate manual and sensory systems, providing comprehensive passenger analysis with higher precision than individual methods could achieve alone.
3Measurement precision
If vision-based detection with machine learning models is implemented, then passenger attribute determination accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces trained machine learning models as intermediary components between the camera input and the airbag control decision. These pre-trained models process the complex image analysis tasks, allowing the control system to make accurate decisions without requiring complex real-time processing logic in the control unit itself.
Data Source
AI summary
Vision-based airbag enablement may include capturing two-dimensional images of a passenger, segmenting the image, classifying the image, and determining seated height of the passenger from the image. Enabling or disabling deployment of the airbag may be controlled based at least in part upon the determined seated height.