AI Sensor Cueing for Data Relevance and Storage Reduction
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Solution Overview
Problem
Conventional AI systems face challenges in efficiently processing and transferring large amounts of sensor data, leading to delayed responses and increased data storage requirements, while also struggling with sensor biases and erroneous information.
Innovation Solution
The implementation of a system and method for sensor cueing using AI models, where a computing model generates a sensor command based on received sensor data, including object and sensor parameters, to improve sensor performance by correcting biases and removing erroneous information, and dynamically managing AI models and sensors for enhanced data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI inference processes use a large number of computing resources to analyze sensor data, then object detection and prediction accuracy is improved, but data processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by generating sensor commands based on model inferences before full data processing is complete. The sensor cueing system uses initial sensor data and AI model predictions to proactively generate commands that guide subsequent data collection, reducing the need to process all raw sensor data while maintaining detection accuracy.
Solution Approach 2:
The system extracts only the essential information needed for accurate detection by using AI models to identify key features and generate targeted sensor commands. Instead of processing all sensor data, the system extracts relevant object parameters and uses them to guide selective data collection, reducing processing time while maintaining precision.
2Measurement precision
If sensor data is collected continuously to improve detection accuracy, then measurement precision is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the necessary sensor data for accurate detection by using AI model inferences to identify relevant object parameters. The sensor cueing system generates commands that request only the specific data needed for detection tasks, rather than continuously collecting and storing all sensor data, thereby reducing storage requirements while maintaining detection precision.
Solution Approach 2:
The system applies local quality by directing sensor data collection focus to specific areas or parameters identified by AI models. Instead of uniform continuous collection across all sensors, the system generates targeted sensor commands that concentrate data collection on relevant objects and parameters, improving detection accuracy while minimizing overall data volume and storage needs.
3Measurement precision
If sensor parameters are adjusted dynamically to improve data relevance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements feedback by using AI model inferences to generate sensor commands that dynamically adjust sensor parameters based on detected objects and contexts. The sensor cueing system creates a closed-loop system where model predictions inform sensor configuration, which in turn generates data for further model analysis, improving data relevance while automating the complexity management through intelligent algorithms.
Solution Approach 2:
The system applies self-service by enabling the AI models to automatically generate sensor commands and adjust sensor parameters without manual intervention. The sensor cueing system allows the AI models to self-manage the complexity of sensor coordination, dynamically optimizing sensor parameters based on real-time inferences and object detection, thereby improving data relevance while reducing operational complexity.
Data Source
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AI summary
Disclosed herein are systems and methods for sensor cueing. In one example, the method includes: receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object; generating a sensor command based at least in part upon the model inference, the sensor command comprising one or more object parameters associated with the target object and one or more sensor parameters associated with a sensor; and transmitting the sensor command to the sensor via a sensor API.