Distributed Avionics Processing for Urban Air Mobility
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Solution Overview
Problem
Urban air mobility (UAM) vehicles face challenges in hosting numerous systems and processing power due to constrained resources like energy, payload, and weight, while maintaining stringent safety standards. Additionally, navigation in UAM environments is hindered by limited GPS availability, signal degradation, and obstacles like buildings and fog.
Innovation Solution
The implementation of distributed vehicle processing systems that offload computation, storage, and data functions to edge nodes and cloud services. This approach allows UAM vehicles to perform only time-sensitive tasks onboard, while less critical processes are handled by edge nodes and cloud services, reducing resource requirements and enhancing navigation through cross-referenced supplemental navigation methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If numerous systems and processing power are hosted on-board UAM vehicles, then navigation and safety functions can be performed, but resource constraints (energy, payload, weight) are exceeded
Solution Approach 1:
The patent segments the processing architecture into three distinct layers: on-board vehicle processing for time-critical functions, edge node processing for intermediate functions, and cloud processing for non-time-critical functions. This segmentation allows safety-critical navigation functions to remain on-board while offloading other processing to reduce energy consumption and resource requirements.
Solution Approach 2:
The patent extracts non-time-critical processing functions from the on-board vehicle system and relocates them to edge nodes and cloud infrastructure. This extraction reduces the computational burden and energy consumption of the on-board system while maintaining overall system functionality and safety standards.
2Reliability
If numerous systems and processing power are hosted on-board UAM vehicles, then navigation and safety functions can be performed, but payload and weight constraints are exceeded
Solution Approach 1:
The patent segments the processing architecture into three distinct layers: on-board vehicle processing for time-critical functions, edge node processing for intermediate functions, and cloud processing for non-time-critical functions. This segmentation allows safety-critical navigation functions to remain on-board while offloading other processing to reduce energy consumption and resource requirements.
Solution Approach 2:
The patent extracts non-time-critical processing functions from the on-board vehicle system and relocates them to edge nodes and cloud infrastructure. This extraction reduces the computational burden and energy consumption of the on-board system while maintaining overall system functionality and safety standards.
3Measurement precision
If GPS and optical ranging are used for navigation in UAM environment, then position information can be obtained, but signal availability is limited and quality is degraded
Solution Approach 1:
The patent merges multiple navigation data sources including GPS, optical ranging, cellular network data, and terrain information into a unified navigation solution. This fusion approach compensates for the limitations of individual systems, maintaining navigation accuracy even when GPS or optical signals are degraded or unavailable due to urban canyons, buildings, or weather conditions.
Solution Approach 2:
The patent introduces edge nodes as intermediary components that provide supplemental navigation information and act as mediators between the vehicle and cloud-based navigation services. These edge nodes can provide local mapping data and navigation assistance when satellite signals are blocked, effectively bridging the gap between on-board sensors and external navigation infrastructure.
Data Source
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AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for distributed vehicle processing. For instance, the method may include: in response to determining a first trigger condition of a first set of trigger conditions is satisfied, performing a first process corresponding to the first trigger condition on-board a vehicle; in response to determining a second trigger condition of a second set of trigger conditions is satisfied, prompting a second process corresponding to the second trigger condition by transmitting an edge request to an edge node and receiving an edge response from the edge node; and in response to determining a third trigger condition of a third set of trigger conditions is satisfied, prompting a third process corresponding to the third trigger condition by transmitting a cloud request to a cloud node and receiving a cloud response from the cloud node.