Interactive merging behavior in a coupled driving simulator: Experimental framework and case study
DOI: 10.54941/ahfe1002485
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Summary
This paper addresses the challenge of understanding human-human interactive merging behavior, a critical component for developing autonomous vehicles (AVs) that interact safely with human drivers. Existing research has largely focused on single-driver behaviors or simulated interactions, lacking the ability to capture the dynamic, joint behavior of two interacting drivers in controlled settings. To bridge this gap, the authors propose an experimental framework consisting of a simplified merging scenario and three novel analysis tools designed to quantify interaction dynamics. The study was conducted using a coupled driving simulator where two human participants interacted in a top-down view. The experimental design simplified the action space to longitudinal control only, removing steering and right-of-way rules to isolate interaction dynamics. The scenario involved two vehicles approaching a merge point, with participants instructed to maintain initial velocity while avoiding collisions. A case study involved 18 participants (9 pairs) who completed 110 trials across 11 experimental conditions, varying initial relative velocity and projected headway. The authors introduced three analysis tools: a visual representation plotting headway against average traveled distance to capture joint trajectories and safety margins; a "level-of-conflict" signal quantifying the effort required to resolve the conflict; and a metric called Conflict Resolution Time (CRT), measuring the time taken to exit a collision course. The results demonstrated that the proposed framework effectively exposed diverse behaviors across different conditions. The visual representation and conflict signals provided deeper insight into individual trial dynamics than traditional position and velocity plots, clearly showing how conflicts were resolved and when safety margins were established. The CRT metric revealed aggregate differences between conditions; specifically, conflicts were resolved fastest when one vehicle had a clear headway advantage, while the condition with equal velocity and headway (0_0) resulted in the highest median CRT, indicating greater difficulty in resolving the interaction. The significance of this work lies in providing a robust methodological foundation for studying interactive driving behavior. The proposed tools allow for systematic comparison of joint human behavior, which is essential for developing accurate driver models and designing AVs that can predict and respond to human interactions. While the authors note limitations regarding the simplified scenario's relation to natural driving and the need to extend tools to 3D environments, the framework offers a valuable asset for future human-factors research in transportation.
Key finding
The proposed experimental framework and analysis tools successfully quantify interactive merging dynamics, revealing that conflict resolution time is shortest when one vehicle holds a distinct advantage in projected headway or velocity.
Methodology
simulator
Sample size: 18
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-05 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-05 |
| chunk | success | chunk | — | — | 1 | 2026-06-05 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-05 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model