Meta-Sensor Fusion for Low-Latency Autonomous Decisions
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Solution Overview
Problem
Current autonomous systems face challenges in efficiently processing the vast amount of data generated by multiple sensors, leading to latency in decision-making and potential conflicts in sensor data interpretation.
Innovation Solution
The development of a meta-sensor system that combines data from multiple sensors and applies preprocessing techniques to fuse sensor information at the point of sensing, reducing data volume and eliminating conflicts through real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to cover various sensing modes, then the sensing capability and detection accuracy are improved, but the amount of data generated increases significantly, leading to processing latency
Solution Approach 1:
The patent combines multiple sensors of different modalities (imaging, depth, thermal, etc.) into a single sensor array where each pixel location contains multiple sensing elements. This merging approach allows simultaneous capture of multiple data types at the same spatial location, improving sensing capability while reducing the overall data volume compared to using separate sensor systems, thereby addressing the contradiction between enhanced measurement precision and reduced processing latency
Solution Approach 2:
The sensor array is segmented into multiple pixel types (imaging pixels, depth pixels, thermal pixels, etc.) that are distributed across the sensor surface. Each pixel type is optimized for specific sensing functions, allowing the system to capture only relevant data for each modality separately rather than processing all sensor data uniformly, thus reducing processing latency while maintaining comprehensive sensing capability
2Adaptability or versatility
If independent data streams are generated from each sensor, then the functionality and versatility of the system are improved, but the computational resources required for processing increase dramatically
Solution Approach 1:
Multiple sensing modalities are merged into a single integrated sensor array with shared readout electronics and processing circuitry. This consolidation maintains the functionality of independent sensor types while dramatically reducing the computational overhead associated with managing separate data streams, as the system processes unified data from a single sensor platform rather than coordinating multiple independent sensors
Solution Approach 2:
The sensor array is designed as a universal platform that can perform multiple sensing functions simultaneously through different pixel types. Each pixel location can be configured for different modalities, allowing the same physical sensor to adaptively serve multiple functions based on operational requirements, thereby maintaining versatility while reducing device complexity through a single multi-functional system
3Ease of operation
If sensor data is processed separately and then fused post-processing, then the independence and modularity of sensor processing are improved, but conflicts in data interpretation arise and decision-making latency increases
Solution Approach 1:
The sensor array performs preliminary data fusion at the pixel level by simultaneously capturing multiple modalities at each spatial location. This preliminary action occurs before data leaves the sensor, ensuring that conflicting information is resolved at the source rather than later during post-processing, thereby improving data interpretation accuracy while maintaining the modular structure of the sensor array
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the computational resources required for data processing, enables quick and accurate decision-making in real-time, and enhances the ability of autonomous systems to operate like human perception systems.
Implementation Method 1
each pixel in the array is connected to an optical to electrical converter that generates an electrical signal representing information about the object
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.