Cabin Activity Detection Device Local Remote Analysis
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Solution Overview
Problem
There is a need for a system that can detect and respond to activities within a vehicle cabin, such as left-behind items or pets, to alert the owner and potentially take actions to ensure safety, as existing solutions lack comprehensive and efficient monitoring capabilities.
Innovation Solution
A cabin activity detection system that includes sensors and a local analysis engine to process data and perform actions, such as rolling down windows or alerting the owner, and can also send data to an external analysis device for further processing if necessary, utilizing a network interface to connect with local action devices and remote receivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive sensor monitoring and analysis capabilities are implemented to detect all cabin activities, then detection reliability and safety monitoring are improved, but device complexity and energy consumption increase
Solution Approach 1:
The system segments detection responsibilities between local analysis (performed in the vehicle) and remote analysis (performed in the cloud). The local analysis engine handles basic sensor data processing and simple detection tasks, while the remote server handles complex analysis requiring more computational resources. This segmentation allows comprehensive monitoring without requiring all processing capabilities to be present in the vehicle, thus improving reliability while managing device complexity.
Solution Approach 2:
The system performs preliminary local analysis of sensor data before potentially transmitting to remote servers. The local analysis engine pre-processes sensor data, filters out obvious cases, and only transmits uncertain or complex cases to the remote server. This preliminary action reduces the burden on the remote system and allows faster local response times for simple detection tasks, improving overall system reliability without proportionally increasing complexity.
2Productivity
If continuous sensor data processing and analysis are performed locally, then response speed and productivity are improved, but energy consumption increases
Solution Approach 1:
The system performs partial local analysis rather than complete analysis for all sensor data. The local analysis engine performs basic processing on all data and more intensive analysis only when needed (e.g., when detection confidence is low or when critical events are detected). This partial action approach maintains fast response speeds for common scenarios while reducing energy consumption by avoiding unnecessary intensive processing.
Solution Approach 2:
The system uses periodic analysis intervals and event-triggered transmission rather than continuous processing. Sensor data is processed locally at regular intervals or when specific events occur, and only selected data is transmitted to the remote server. This periodic action maintains productivity for time-critical local responses while significantly reducing energy consumption compared to continuous bidirectional processing.
3Measurement precision
If all sensor data is transmitted to remote servers for analysis, then measurement precision and detection accuracy are improved, but loss of time and communication dependency increase
Solution Approach 1:
The system segments detection tasks by confidence level and complexity. High-confidence detections are handled locally with immediate response, while low-confidence or complex cases are transmitted to remote servers for more precise analysis. This segmentation ensures that time-critical decisions are made locally without communication delay, while still utilizing remote resources for improved accuracy when needed.
Solution Approach 2:
The local analysis engine performs preliminary analysis and filtering of sensor data before transmission. It pre-processes data to extract only the most relevant information and transmits reduced datasets to remote servers. This preliminary action reduces communication time and data transmission requirements while maintaining detection accuracy by ensuring that only critical data requiring remote analysis is sent.
4Reliability
If multiple sensors and analysis engines are deployed, then detection coverage and reliability are improved, but loss of substance and system resources increase
Solution Approach 1:
The system uses multi-functional sensors and processing units that can handle multiple sensor types and analysis tasks. The local analysis engine can process data from various sensor modalities (audio, video, environmental sensors) using the same processing framework. This universality allows comprehensive detection coverage across multiple sensor types without proportionally increasing system resources, as the same hardware resources serve multiple detection functions.
Data Source
AI summary
A method system, and non-transitory computer readable medium for cabin activity detection. In one or more embodiments of the invention, the method includes receiving first sensor data at a cabin activity detection device from a first sensor of a plurality of sensors; performing, by the cabin activity detection device, local analysis of the first sensor data to make a determination that a condition is met; and performing a first local action based on the determination that the condition is met.