Latest revision as of 10:13, 15 February 2021

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

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

• http://dx.doi.org/10.3390/systems7010001 under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/2079-8954/7/1/1/pdf under the license https://creativecommons.org/licenses/by/4.0

• http://www.mdpi.com/2079-8954/7/1/1,

https://doaj.org/toc/2079-8954 under the license cc-by

• http://www.mdpi.com/2079-8954/7/1/1/pdf,

http://dx.doi.org/10.3390/systems7010001

• https://www.mdpi.com/2079-8954/7/1/1,

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/