Heterogeneous Sensor Array Compensation for Sensor Failure
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Solution Overview
Problem
Autonomous or semi-autonomous systems relying on heterogeneous sensor arrays face operational failures when one sensor malfunctions, leading to complete loss of capabilities in algorithms dependent on that sensor.
Innovation Solution
A perception engine aggregates data from multiple sensors to detect deficiencies and generates modeled sensor data using a sensor translation model, allowing the system to continue functioning by converting data from functioning sensors to simulate the missing sensor's data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a heterogeneous sensor array is used for autonomous navigation, then the system can perceive and map the surroundings with multiple sensor types, but the system loses complete capability if one sensor malfunctions
Solution Approach 1:
The patent creates a virtual copy of the malfunctioning sensor's data by translating data from functioning sensors through a sensor translation model. This synthetic sensor data replicates the output of the failed sensor, allowing algorithms to continue operating without redundancy hardware.
Solution Approach 2:
The sensor translation model acts as an intermediary between functioning sensors and algorithms that depend on the malfunctioning sensor. It translates data from available sensors into the format expected by algorithms, bridging the gap caused by sensor failure.
2Measurement precision
If algorithms are designed to depend on specific sensor types, then the algorithms can achieve precise functionality, but the system becomes vulnerable to complete capability loss when sensors fail
Solution Approach 1:
The system dynamically adapts its data sources based on sensor availability. When a sensor fails, the sensor translation model dynamically switches to using data from other sensors, transforming it into the required format. This dynamic reconfiguration maintains algorithm precision without requiring pre-programmed fallbacks for each failure scenario.
Solution Approach 2:
The sensor translation model changes the parameters of available sensor data to match the expected parameters of the malfunctioning sensor. By transforming data characteristics (format, resolution, temporal properties), the system maintains compatibility with algorithms designed for specific sensor types.
3Reliability
If redundant sensors are added to compensate for potential failures, then system reliability improves, but device complexity and cost increase
Solution Approach 1:
The sensor translation model enables existing sensors to serve multiple functions. By translating data between different sensor types, a single sensor can compensate for the failure of another sensor type, eliminating the need for dedicated redundant sensors for each failure mode.
Solution Approach 2:
Instead of physically copying sensors for redundancy, the system creates virtual copies of sensor data through translation. This synthetic data replication provides redundancy without the physical overhead of additional sensors.
Data Source
AI summary
Apparatuses, methods and storage medium associated with compensating for a sensor deficiency in a heterogeneous sensor array are disclosed herein. In embodiments, an apparatus may include a compute device to aggregate perception data from individual perception pipelines, each of which is associated with respective one of different types of sensors of a heterogeneous sensor set, to identify a characteristic associated with a space to be monitored by the heterogeneous sensor set; detect a sensor deficiency associated with a first sensor of the sensors; and in response to a detection of the sensor deficiency, derive next perception data for more than one of the individual perception pipelines from sensor data originating from at least one second sensor of the sensors. Other embodiments may be disclosed or claimed.