In-Cabin Object Detection for Vehicle Projectile Risk Mitigation
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Solution Overview
Problem
Current vehicle safety systems fail to effectively predict and mitigate the danger posed by loose objects in a vehicle's cabin during accidents, which can become projectiles and cause injuries due to their size, weight, and location.
Innovation Solution
A system utilizing sensors like radar, cameras, and microphones to detect objects in the vehicle cabin, determine their characteristics such as weight, size, and location, and send notifications to occupants or devices to secure potentially dangerous items based on their danger level, which is calculated using machine learning algorithms and object detection software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle safety systems use basic detection methods, then the system complexity is low, but the ability to predict and mitigate projectile danger is insufficient
Solution Approach 1:
The system segments the detection and assessment process into distinct modules: object detection sensors (radar, cameras, microphones), characteristic analysis (weight, size, location), danger level calculation using machine learning algorithms, and notification systems. This segmentation allows each component to specialize in specific functions, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw sensor data and safety decisions. These algorithms process object characteristics and calculate danger levels, acting as a mediator that transforms basic detection data into predictive safety assessments without requiring direct complex hardware interventions.
2Reliability
If the system monitors all objects in the vehicle, then the safety coverage is comprehensive, but the processing time and computational load increase
Solution Approach 1:
The system applies local quality by focusing monitoring resources on objects with higher danger characteristics. Rather than treating all objects uniformly, the machine learning algorithms identify and prioritize objects based on their specific properties (weight, size, location), allocating processing power selectively to those most likely to become projectiles, thus maintaining comprehensive safety coverage while reducing overall processing time.
3Loss of information
If the system provides detailed notifications to occupants, then the information completeness is high, but the ease of operation for occupants decreases
Solution Approach 1:
The system implements feedback by providing notifications to occupants about detected dangerous objects and recommended actions. This feedback loop allows occupants to respond appropriately to safety warnings while the system continues to monitor and update information, balancing detailed information provision with clear, actionable guidance that maintains ease of operation.
Data Source
AI summary
An example operation includes one or more of determining a danger level of an object in a vehicle, determining characteristics of the object including a weight, a size, and a location, updating the danger level of the object based on one or more of the characteristics, and sending a notification to a device associated with the updated danger level of the object.