Adaptive software platform architecture for aerial vehicle safety levels in real-world applications

Rakesh Shrestha, Rojeena Bajracharya, Shiho Kim

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Urban cities have congested with vehicles, resulting in traffic jams, and a lot of time and energy will be wasted while traveling. In the near future, low-altitude aerial vehicles are expected to be implemented for air traffic as a resolution to overcome these issues. Due to the threats to commercial aircraft as well as danger to the public and objects on the ground, these low-altitude aerial vehicles should exhibit an equivalent level of safety similar to commercial aircraft. We propose a new safety level for low-altitude electric propeller-based compact aerial vehicles. The safety level of Aerial Vehicles is based on the concept of civil aviation safety levels and automotive safety levels, and we incorporate the appropriate safety characteristics from both automotive safety integrity levels and aviation safety levels. We also discussed adjusting the aerial vehicle safety levels with NASA's Technical Capability Levels (TCL), which helps design an Electrical and Electronics (E/E) architecture for the aerial vehicles. We presented a new conceptual E/E architecture for the aerial vehicles based on adjusted technical capabilities levels and aerial vehicle safety levels to provide functional safety for the aerial vehicle. We also discuss the adaptive software platform based on virtualization, which partitions the time-critical operating system of aerial vehicles to host several applications of different software levels on the same hardware.

Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning for Open-world Novelty
EditorsShiho Kim, Ganesh Chandra Deka
PublisherAcademic Press Inc.
Pages51-90
Number of pages40
ISBN (Print)9780323999281
DOIs
Publication statusPublished - 2024 Jan

Publication series

NameAdvances in Computers
Volume134
ISSN (Print)0065-2458

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • General Computer Science

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