Abstract

Advanced driver assistance and automated driving systems must operate in complex environments and make safety-critical decisions. Resilient behavior of these systems in their targeted operation design domain is essential. In this paper, we describe developments in our Model-Based Systems Engineering (MBSE) approach to develop resilient safety-critical automated systems. An MBSE approach provides the ability to provide guarantees about system behavior and potentially reduces dependence on in-vehicle testing through the use of rigorous models and extensive simulation. We are applying MBSE methods to two key aspects of developing resilient systems: (1) ensuring resilient behavior through the use of Resilience Contracts for system decision making; and (2) applying simulation-based testing methods to verify the system handles all known scenarios and to validate the system against potential unknown scenarios. Resilience Contracts make use of contract-based design methods and Partially Observable Markov Decision Processes (POMDP), which allow the system to model potential uncertainty in the sensed environment and thus make more resilient decisions. The simulation-based testing methodology provides a structured approach to evaluate the operation of the target system in a wide variety of operating conditions and thus confirm that the expected resilient behavior has indeed been achieved. This paper provides details on the development of a utility function to support Resilience Contracts and outlines the specific test methods used to evaluate known and unknown operating scenarios.

Document type: Article

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Original document

The different versions of the original document can be found in:

https://doaj.org/toc/2079-8954 under the license cc-by
http://dx.doi.org/10.3390/systems7010001
https://www.mdpi.com/2079-8954/7/1/1/pdf,
https://doi.org/10.3390/systems7010001,
https://dblp.uni-trier.de/db/journals/systems/systems7.html#DAmbrosioAOPRMS19,
https://academic.microsoft.com/#/detail/2909109982 under the license https://creativecommons.org/licenses/by/4.0/
Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3390/systems7010001
Licence: Other

Document Score

0

Views 7
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?