Vehicle Cabin Occupant Detection for Real-Time Status Alerts
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Solution Overview
Problem
Existing technologies lack an efficient method to monitor and communicate the status of a vehicle's cabin, including occupant characteristics and cabin conditions, to mobile devices.
Innovation Solution
A system that receives a scan of a vehicle's cabin, determines occupant characteristics through classification and presence detection, and communicates the cabin status to a mobile device, utilizing a processor and memory for data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a system is implemented to monitor and communicate cabin status in real-time, then safety and convenience are enhanced through accurate occupant information, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the monitoring task by dividing the cabin into multiple zones (driver zone, passenger zones, trunk, etc.) and processes each zone independently through separate detection algorithms. This modular approach enhances monitoring accuracy for each specific area while managing overall system complexity through organized, manageable segments.
Solution Approach 2:
The system introduces an intermediary processing layer that acts as a mediator between raw sensor data and final cabin status determination. This intermediary layer filters, integrates, and interprets data from multiple sources before communicating results, thereby improving reliability while abstracting away the complexity from both data collection and output stages.
2Measurement precision
If multiple detection algorithms are used to determine occupant characteristics, then measurement precision is improved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and pre-classifying potential occupant characteristics before final analysis. Detection algorithms are pre-configured with expected patterns and thresholds, allowing the system to quickly match incoming data against known profiles, thereby improving measurement precision while reducing real-time processing time.
Solution Approach 2:
The system applies partial detection strategies by focusing computational resources on the most critical occupant characteristics and zones based on current context. Rather than continuously analyzing all possible parameters at full depth, the system dynamically adjusts the level of analysis, performing exhaustive detection only when necessary and using simplified algorithms for routine monitoring, thus balancing precision with processing speed.
Data Source
AI summary
An example operation includes one or more of receiving a scan of a cabin of a vehicle, wherein the scan includes a spatial region inside the vehicle and outside the vehicle proximate at least one vehicle door, determining at least one occupant characteristic based on the scan, including a classification detection and a presence detection of at least one seat inside the vehicle, determining a cabin status based on the at least one occupant characteristic and communicating the cabin status to a mobile device.