ORCID Number

0000-0003-0454-5529

Date of Award

Spring 2026

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Electrical Engineering & Computer Science

Department

Electrical Engineering and Computer Science

Committee Chair

M. Ilhan Akbas

Committee Chair Email

akbasm@erau.edu

First Committee Member

Eduardo Rojas

First Committee Member Email

rojase1@erau.edu

Second Committee Member

Laxima Niure Kandel

Second Committee Member Email

niurekal@erau.edu

Third Committee Member

Richard S. Stansbury

Third Committee Member Email

stansbur@erau.edu

Fourth Committee Member

Raivo Sell

Fourth Committee Member Email

raivo.sell@taltech.ee

College Dean

James W. Gregory

Abstract

Today is an age of exciting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves constructing relevant and realistic scenarios to evaluate the performance of a complex system through observation, is a practical and effective means of V\&V testing under time and resource constraints. Furthermore, scenario-based V\&V testing has been the hallmark approach when testing the AV's predecessor, the automobile, as such it is the natural progression to perpetuate scenario-based V\&V testing for AVs. As such, this dissertation proposes an end-to-end scenario-based V\&V testing framework for AV V\&V testing, which intelligently and effectively identifies and proposes solutions to the challenges of testing AVs and other complex systems under time and resource constraints. The dissertation begins by identifying sources of AV V\&V scenarios, then constructing modular and measurable scenarios that are validated through scenario-based tests. Then, challenges in scenario-based testing of highly complex, high-dimensional scenarios are identified through modeling. This dissertation includes numerous studies of sequence sampling and exploration strategies to efficiently explore high-dimensional spaces, and to select high-value and edge scenarios. After the foundations of the proposed framework are validated, the dissertation diversifies: a first-of-it's kind AV scenario-description language for network-based traffic simulation is proposed, a practical means of estimating scenario complexity of AV V\&V scenarios is proposed, and interpretation of the scenario test data by leveraging explainable artificial intelligence is proposed. Finally, the framework is stress tested via highly complex, high-dimensional scenario tests to evaluate the scalability, interpretability, and performance of the dissertation research.

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GS9_Acceptance_gossq.pdf (439 kB)
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