Automated simulation of scenarios to guide the development of a crosswind stabilization function

Hilf, Klaus-Dieter; Matheis, Ingo; Mauss, Jakob; Rauh, Jochen

doi:10.3182/20100712-3-de-2013.00195

Cited by 5 publications

(1 citation statement)

References 3 publications

(2 reference statements)

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“…While there has been a large amount of work performed in both model-based testing and SBT areas, there are only a small number of examples of using these techniques together-that is, the use of SBT principles applied to closed-loop model-based testing. One prominent example in this field is the TestWeaver software package [26][27][28][29][30]. Test case generation is described as playing a game against the SuT with the intention to drive it to a state that violates its specifications.…”

Section: Previous Uses Of Search-based Testing For Autonomousmentioning

confidence: 99%

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

Betts

Petty

2016

Modelling and Simulation in Engineering

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Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.

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Section: Previous Uses Of Search-based Testing For Autonomousmentioning

confidence: 99%

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

Betts

Petty

2016

Modelling and Simulation in Engineering

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A Method for an Efficient, Systematic Test Case Generation for Advanced Driver Assistance Systems in Virtual Environments

Schuldt

Reschka²,

Maurer³

2017

Automotive Systems Engineering II

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Automated Generation of Fault Scenarios to Assess Potential Human Errors and Functional Failures in Early Design Stages

Irshad

Demirel

Tumer

2020

Journal of Computing and Information Science in Engineering

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Abstract Human errors are attributed to a majority of accidents and malfunctions in complex engineered systems. The human error and functional failure reasoning (HEFFR) framework was developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging for engineers. This research aims overcome this limitation by creating a use case generation technique that covers both component- and human-related fault scenarios. The proposed technique is a time-based simulation that employs a modified depth first search (DFS) to simulate events as the event propagation is analyzed using HEFFR at each time-step. The results show that the proposed approach is capable of generating a wide variety of fault scenarios involving humans and components. Out of the 15.4 million scenarios that were found to violate the critical function, two had purely human-induced faults, 163,204 had purely non-human-induced faults, and the rest had a combination of both. The results also show that the framework was able to uncover hard-to-detect scenarios such as scenarios with human errors that do not propagate to affect the system. In fact, 86% of all human action combinations with nominal human-induced component behaviors had underlying human errors.

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Automated simulation of scenarios to guide the development of a crosswind stabilization function

Cited by 5 publications

References 3 publications

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

Automated Search-Based Robustness Testing for Autonomous Vehicle Software

A Method for an Efficient, Systematic Test Case Generation for Advanced Driver Assistance Systems in Virtual Environments

Automated Generation of Fault Scenarios to Assess Potential Human Errors and Functional Failures in Early Design Stages

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