Developing an Unreal Engine 4-Based Vehicle Driving Simulator Applicable in Driver Behavior Analysis—A Technical Perspective
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Summary
This paper addresses the need for a customizable, high-fidelity vehicle driving simulator to analyze driver behavior and control performance, motivated by the limitations of commercial simulators which often lack open-source accessibility, custom scenario creation, or sufficient graphical realism. The authors argue that while driving simulators offer safety, reproducibility, and simplified data acquisition compared to real-world testing, existing market solutions are either too expensive, proprietary, or graphically insufficient for detailed research. Consequently, the study focuses on developing an in-house simulator using the Unreal Engine 4 (UE4) framework to facilitate precise driver analysis from a control theory perspective. The methodology involved designing a Vehicle Driving Simulator (VDS) built on UE4, leveraging its physics engine, asset library, and VR support. The system architecture comprises three main components: the Player Vehicle set (using C++ classes and blueprints for vehicle physics based on NVIDIA PhysX), four specific driving scenarios, and complementary gameplay elements. The hardware setup includes a high-performance PC (AMD Ryzen 9, NVIDIA RTX 2080 Super) to maintain a frame rate above 100 Hz, ensuring sufficient sampling frequency for capturing rapid steering corrections. Input devices include a Logitech G920 steering wheel with force feedback and a gear shifter. Software features include curb detection via raycasting, rearview mirror simulation using Scene Capture 2D, and a Heads-Up Display (HUD) providing real-time data on velocity, gear, and lane deviation. The simulator implements four scenarios: a calibration run, a "Step Response" highway test for analyzing lane-change reactions, a long-distance ride to simulate fatigue, and a "Moose Test" to evaluate vehicle dynamics and driver control under ISO 3888-2:2011 standards. The results demonstrate that the in-house simulator successfully meets the requirements for scientific driver analysis, offering high graphical realism, customizable scenarios, and accurate data acquisition. The authors validated that maintaining a frame rate above 100 Hz ensures valid signal reconstruction for human operator reaction delays, which range from 200 to 1500 ms. The system allows for the precise measurement of driver inputs and vehicle responses, enabling the derivation of parameters for a driver performance index. The integration of control theory models treats the driver as a controller within a human-machine loop, allowing for the quantification of skill-based behaviors. The simulator effectively replicates hazardous situations, such as sudden obstacles or lane deviations, in a safe environment, while providing immediate feedback to drivers through visual and audio cues. The significance of this work lies in providing a cost-effective, open-source alternative to proprietary simulators, enabling researchers to create custom measurement scenarios and modify virtual elements to suit specific needs. By establishing a robust platform for acquiring synchronized measurement data, the study facilitates deeper insights into human factor assessment and driver-vehicle interaction. The developed application supports the analysis of driver performance through control theory metrics, contributing to the broader field of vehicle safety and the development of advanced driver assistance systems (ADAS). The simulator’s ability to expose subjects to dangerous tasks safely and reproducibly underscores its value for future research in human automation synergy and driver behavior modeling.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-17 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified_with_issues.
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- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: computational model