Toward Risk-Informed Operation of Autonomous Vehicles to Increase Resilience in Unknown and Dangerous Environments

Mimlitz, Zachary; Short, Adam R.; Bossuyt, Douglas L. Van

doi:10.1115/detc2016-60002

Cited by 4 publications

(2 citation statements)

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“…However, during mission execution, an adapted version of this method may be useful for UAS to execute autonomously as battlefield situations change. Indeed, such autonomous system behaviors have been suggested in space systems contexts in the past [30,31,67,68] although without the use of a DT.…”

Section: Discussionmentioning

confidence: 99%

Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning

Lee

Bossuyt

Bickford

2021

Systems

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This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria.

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Section: Discussionmentioning

confidence: 99%

Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning

Lee

Bossuyt

Bickford

2021

Systems

Self Cite

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“…This can have a detrimental effect on human life, property, etc. Thus, studies have been conducted to solve these problems [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Identification of untrained class data using neuron clusters

Lee

Chae

2023

Neural Comput & Applic

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Convolutional neural networks (CNNs), a representative type of deep neural networks, are used in various fields. There are problems that should be solved to operate CNN in the real-world. In real-world operating environments, the CNN’s performance may be degraded due to data of untrained types, which limits its operability. In this study, we propose a method for identifying data of a type that the model has not trained on based on the neuron cluster, a set of neurons activated based on the type of input data. In experiments performed on the ResNet model with the MNIST, CIFAR-10, and STL-10 datasets, the proposed method identifies data of untrained and trained types with an accuracy of 85% or higher. The more data used for neuron cluster identification, the higher the accuracy; conversely, the more complex the dataset's characteristics, the lower the accuracy. The proposed method uses only the information of activated neurons without any addition or modification of the model’s structure; hence, the computational cost is low without affecting the classification performance of the model.

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