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Erschienen in: International Journal on Software Tools for Technology Transfer 2/2016

01.04.2016 | TACAS 2014

Probabilistic verification and synthesis of the next generation airborne collision avoidance system

verfasst von: Christian von Essen, Dimitra Giannakopoulou

Erschienen in: International Journal on Software Tools for Technology Transfer | Ausgabe 2/2016

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Abstract

The next generation airborne collision avoidance system, ACAS X, departs from the traditional deterministic model on which the current system, TCAS, is based. To increase robustness, ACAS X relies on probabilistic models to represent the various sources of uncertainty. The work reported in this paper identifies verification challenges for ACAS X, and studies the applicability of probabilistic verification and synthesis techniques in addressing these challenges. Due to shortcomings of off-the-shelf probabilistic analysis tools, we developed a new framework, named VeriCA (Verification for Collision Avoidance). VeriCA is a combined probabilistic synthesis and verification framework that is custom designed for ACAS X and systems with similar characteristics. VeriCA supports Java as a modeling language, is memory efficient, employs parallelization, and provides an interactive simulator that displays aircraft encounters and the corresponding ACAS X behavior. We describe the application of our framework to ACAS X, together with the results and recommendations that our analysis produced.

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Fußnoten
1
As explained in [9], the advisory response is determined according to a Bernoulli process, so the delay until response follows a geometric distribution. If the response probability at each step in the process is \(\frac{1}{1+k}\), then the mean time until response is k.
 
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Metadaten
Titel
Probabilistic verification and synthesis of the next generation airborne collision avoidance system
verfasst von
Christian von Essen
Dimitra Giannakopoulou
Publikationsdatum
01.04.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Software Tools for Technology Transfer / Ausgabe 2/2016
Print ISSN: 1433-2779
Elektronische ISSN: 1433-2787
DOI
https://doi.org/10.1007/s10009-015-0388-8

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