Distributed Moving Base Driving Simulators : Technology, Performance, and Requirements
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Summary
This dissertation investigates whether distributed moving base driving simulators can maintain high fidelity while offering greater flexibility for testing emerging vehicle technologies, such as connected and autonomous systems. The research is motivated by the accelerating pace of vehicle development, which requires evaluation tools that are both credible and adaptable. Traditional simulators often struggle with maintenance and integration of new hardware or software components. The central research question is whether dividing a simulator’s vehicle model into subsystems running on separate networked nodes (distributed simulation) can facilitate maintenance and development without compromising the realism required for valid driver behavior studies. To address this, the author designed, built, and analyzed three distinct distributed simulator solutions. These setups included external hardware integration via hardware-in-the-loop techniques and utilized the High Level Architecture (HLA) for communication between nodes. A key component of the study was the development of a parameterized powertrain model capable of representing various vehicle types. The research also explored the use of Modelica, an equation-based object-oriented modeling language, to simulate subsystems and manage model requirements. The experimental design involved transferring subsystems to a Modelica environment to create a framework for automatically evaluating model realism and detecting deviations. The study specifically examined the impact of network latency on driving behavior and the necessity of requirement management methodologies to ensure model integrity after modifications. The results indicate that the developed distributed simulators performed well with satisfactory overall performance. In setups with fast networks, communication delays were negligible and did not affect the driving experience. However, the study found that gradually increasing network latency caused drivers to alter their behavior, demonstrating that the impact of latency depends on the specific simulator application and study demands. The research highlighted that modifications to system components can inadvertently affect vehicle model behavior, necessitating robust methods for managing model requirements. To address this, the author implemented a monitoring aid to detect when a model behaves unexpectedly or operates outside its validated region. Furthermore, the implementation of the powertrain model in Modelica, extended with requirements management, successfully demonstrated a framework for automatically evaluating model quality within the simulation tool. The significance of this work lies in providing a validated approach for constructing distributed driving simulators that maintain high fidelity comparable to traditional setups. By establishing that distributed architectures can support realistic evaluation while offering modular flexibility, the research supports the efficient testing of complex, evolving vehicle systems. The proposed framework for automatic requirement verification and model monitoring addresses critical challenges in maintaining simulator credibility during component updates. This contributes to the field by offering methodologies to ensure that distributed simulators remain reliable tools for investigating driver support systems and autonomous vehicle functionalities, thereby facilitating faster and more adaptable vehicle development cycles.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-17 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: computational model