Driving Simulator Project
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Summary
This report details the development and application of a comprehensive driving and pedestrian simulation system by the Transportation Research Center at the University of Nevada, Las Vegas, sponsored by the Nevada Department of Transportation. The project addresses the critical need for understanding pedestrian dynamics and distracted driving behaviors to improve transportation safety. Motivated by high pedestrian fatality rates and the limitations of existing mathematical models that fail to capture diverse human factors, the research aims to create a safe, controlled environment for analyzing human-vehicle interactions. The system integrates hardware and software components to simulate realistic driving and walking scenarios. The driving simulator utilizes a SimCraft Technologies 3-degree-of-freedom motion platform with a Recarro seat, wheels for mobility, and a three-monitor setup providing a 120-degree field of view. Software scenarios are generated using STISIM Drive, which allows for customizable roadway geometries, traffic controls, and vehicle dynamics. A key innovation is the development of a pedestrian simulator module that tracks human walk gestures and maps them to virtual entities. This module explores both Arduino-based wearable interfaces and Microsoft Kinect-based video interfaces, ultimately selecting the Kinect for its ease of calibration. The system employs a mathematical framework to abstract human gait into a non-holonomical dynamical system, enabling real-time integration into the simulation engine. The report presents findings from experimental setups focused on distracted driving, specifically analyzing the effects of inattention on vehicle control. Data collection involved recording lateral lane position (LLP), steering wheel angle (SWA), and speed of vehicle (SOV). Analysis methods included time-domain standard deviation, frequency-domain spectral analysis, and entropy-based analysis. The results demonstrate measurable variations in these parameters under distracted conditions, providing empirical evidence of how inattention impacts driving stability. Additionally, the pedestrian simulator successfully captured and converted human walking parameters into virtual movements, validating the feasibility of using natural interaction technologies for pedestrian behavior modeling. The significance of this work lies in its contribution to transportation safety research and public outreach. The simulator has been deployed in various public campaigns, including demonstrations against texting while driving and drunk driving, supporting Nevada’s "Zero Fatality" goal. By providing a tool that captures real-time human factors in a controlled environment, the project bridges the gap between theoretical models and actual human behavior. The developed API and mathematical frameworks offer a scalable solution for future research into pedestrian dynamics and driver distraction, enhancing the ability to design safer transportation systems and educate the public on safe driving practices.
Key finding
The study developed a functional API and mathematical model to capture human walk gestures via Kinect and map them to virtual pedestrian entities in a driving simulator environment.
Methodology
simulator
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (43 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 40 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- simulator validity fidelity
- situational awareness
- simulator sickness
- simulator training transfer
- steering pattern
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: computational model