Steering a Driving Simulator Using the Queueing Network-Model Human Processor (QN-MHP)
DOI: 10.17077/drivingassessment.1100
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the challenge of modeling complex driving behaviors using computational cognitive architectures. The authors aim to demonstrate the utility of the Queueing Network-Model Human Processor (QN-MHP) for simulating vehicle steering, a foundational step toward modeling more intricate driving scenarios. The QN-MHP combines the mathematical simulation methods of queueing networks with the symbolic procedural methods of GOMS analysis and the Model Human Processor (MHP). This integration allows for the real-time generation of parallel and complex mental activities, addressing limitations in previous models that struggled with concurrent processing. The study was motivated by the need for a computational model that could make quantitative predictions for untested scenarios and provide a precise language for describing human factors in driving. The researchers implemented the QN-MHP steering model using ProModel, a commercial simulation software. The architecture consists of 20 servers representing functional modules of the human perceptual, cognitive, and motor systems. Information entities flow through visual, cognitive, and motor sub-networks, processing vehicle location and orientation data concurrently. The model was interfaced with the DriveSafety Research Simulator via a custom TCP/IP communication protocol and dynamic link libraries (DLLs). This interface allowed real-time data exchange: the simulator sent vehicle state variables to the model, and the model sent steering wheel positions and eye fixation areas back to the simulator. To synchronize the event-based ProModel with the time-based simulator, an external time adjustment function was integrated to align simulated time with actual clock time. The steering logic relied on hierarchical task analysis, utilizing ambient visual systems for lane detection and executing open-loop corrections followed by closed-loop adjustments. The results demonstrated that the QN-MHP successfully steered the driving simulator on a test course comprising straight sections and curves of varying radii. The model maintained the vehicle within lane boundaries, exhibiting realistic steering behavior. The communication between the software modules was smooth with short timing delays, and the continuous flow of information through the servers accurately represented the workload of lane-keeping. The model effectively processed external information and generated appropriate steering actions without requiring manipulation of the underlying architecture, proving the context-free nature of the QN-MHP structure. The significance of this work lies in validating QN-MHP as a robust tool for modeling driving behavior, particularly its ability to handle concurrent perceptual, cognitive, and motor activities without predefining their order. This capability opens avenues for modeling complex, multi-task driving environments. The authors conclude that the successful integration of the model with a high-fidelity simulator allows for real-time demonstration of steering strategies and potential use as an "autopilot." Future work will expand the model to include speed control, traffic-responsive behavior, and secondary in-vehicle tasks, as well as incorporating additional perceptual modalities like auditory and vestibular inputs.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | failed | — | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Theoretical Contribution: computational model, theory or model