Meta-Sensor Fusion for Low-Latency Autonomous Decisions
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Solution Overview
Problem
Autonomous systems face challenges in processing vast amounts of data from multiple sensors, leading to latency and conflicts in decision-making, especially when in motion, due to the need for powerful computational resources and the lack of human-like intelligence in interpreting and fusing sensor data from different modalities.
Innovation Solution
A meta-sensor system that combines data from multiple sensors for preprocessing at the point of sensing, eliminating conflicts and reducing computational requirements by creating a monolithic data stream, and incorporates machine learning to make decisions similar to human perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors of different modalities are used to cover various sensing functions, then the sensing capability and detection accuracy are improved, but the amount of data generated increases massively, leading to processing latency and computational resource requirements
Solution Approach 1:
The patent segments the sensor system into multiple independent sensor devices, each capturing data in its own data stream. This segmentation allows parallel processing of different sensor modalities (image, depth, audio, etc.) independently, reducing the overall processing time compared to handling one massive unified data stream.
Solution Approach 2:
The patent introduces a temporal dimension by capturing multiple frames over time and using motion estimation techniques. This allows the system to differentiate between actual motion and sensor noise, improving detection accuracy while reducing the need for excessive processing of redundant static data.
2Adaptability or versatility
If sensor data is processed separately and then fused post-processing, then each sensor maintains its independent functionality, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent performs preliminary processing of sensor data streams individually before fusion, including motion estimation, noise filtering, and feature extraction. This preliminary action reduces the complexity of subsequent fusion operations by pre-processing each data stream to extract only the most relevant information.
Solution Approach 2:
The patent introduces an intermediary processing layer that handles motion estimation and temporal correlation between frames. This intermediary layer acts as a mediator between individual sensor processing and final data fusion, reducing the computational burden on both ends.
3Reliability
If sensor data from different modalities is fused to make decisions, then the decision accuracy is improved, but conflicts between different sensor interpretations can lead to incorrect decisions
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the consistency between different sensor modalities. When conflicts are detected (e.g., image sensor sees motion but depth sensor doesn't), the system adjusts its interpretation by weighing evidence from multiple sources and using temporal correlation to resolve discrepancies.
Solution Approach 2:
The patent adds the temporal dimension to resolve conflicts between sensor modalities. By analyzing motion across multiple frames and using motion estimation, the system can determine which sensor is more likely to be correct based on consistent motion patterns over time, rather than relying solely on single-frame data from conflicting sensors.
4Productivity
If powerful computer systems are used to process massive sensor data, then the processing capability is improved, but the system becomes dependent on external computational resources and cannot operate autonomously in real-time
Solution Approach 1:
The patent extracts and processes critical information (motion estimation, noise filtering, feature extraction) directly at the sensor level or in close proximity to the sensors. This extraction of essential processing functions from centralized powerful computers enables autonomous real-time operation while still utilizing powerful computation when available for non-critical tasks.
Data Source
AI summary
This invention relates to a sensor and sensor platform, for an autonomous system. The sensor and its platform sense, perform signal or data processing, and make the decision locally at the point of sensing. More specifically, the sensor along with its platform simulates the human-like or human capacity to make decisions by combing the data from several sensors that detect different data sets, and combine them in a series of data processes that allows autonomous decisions to be made. Additionally, the sensor platform combines multiple sensors in one metasensor with the functionality of multiple sensors placed on a common carrier or platform.