Vehicle Perimeter Breach Warning with Size-Based Alert Filtering
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Solution Overview
Problem
Current detection systems, such as smoke detectors and vehicle alert systems, often produce false positives due to the inability to differentiate between dangerous and non-dangerous situations, leading to user frustration and potential deactivation of safety features, which can result in missed critical events and increased nuisance alarms.
Innovation Solution
A system utilizing a combination of sensors and machine learning to accurately classify objects in real-time around a vehicle, filtering out irrelevant data and providing targeted alerts or actions based on the context and intent of detected objects, reducing false positives and minimizing user analysis time while maintaining decision-making control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection systems activate alerts for any detected object or condition, then safety coverage is maximized, but false-positive alerts increase causing user frustration and system deactivation
Solution Approach 1:
The system changes the parameters of detection by transitioning from simple presence detection to multi-parameter analysis including object classification, intent determination, and contextual evaluation. This allows the system to distinguish between dangerous and non-dangerous situations while maintaining comprehensive safety coverage.
Solution Approach 2:
The patent introduces an intermediary classification layer between detection and alerting. This intermediary system evaluates detected objects and situations to determine whether they constitute genuine threats, thereby reducing false positives while maintaining reliable safety coverage through nuanced judgment.
2Loss of information
If detection systems provide comprehensive monitoring of all surroundings, then situational awareness is improved, but user information overload increases
Solution Approach 1:
The system extracts and filters only the most relevant information from comprehensive environmental monitoring. By identifying and highlighting only critical threats and significant events, the system maintains complete situational awareness while presenting manageable information to the user.
Solution Approach 2:
The patent applies local quality by providing different levels of information detail in different contexts. Critical threats receive prominent attention with detailed information, while non-critical elements receive minimal or no attention, optimizing user information processing based on local situational requirements.
3Measurement precision
If alert systems notify users of all detected anomalies, then detection sensitivity is maximized, but user decision-making time increases
Solution Approach 1:
The system performs preliminary classification and evaluation of detected anomalies before presenting them to the user. By pre-processing detection data to identify and prioritize genuine threats, the system maintains high detection sensitivity while significantly reducing the time users need to spend evaluating alerts.
4Reliability
If safety systems activate for any perceived threat, then protection coverage is maximized, but system usability deteriorates
Solution Approach 1:
The system changes operational parameters by implementing multi-factor detection including object classification, intent analysis, and contextual evaluation. This enables the system to maintain comprehensive protection coverage while activating alerts only for genuine threats, thereby preserving system usability.
Data Source
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AI summary
A system for object detection within a monitored zone around a vehicle includes: at least one processor; and memory encoding instructions which, when executed by the processor, cause the system to: detect an object; and suppress an alert associated with detection of the object when a size of the detected object is below a lower size threshold value or above an upper size threshold value.