Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers
DOI: 10.1177/00187208221144561
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Summary
This study investigates the cognitive mechanisms underlying left-turn gap acceptance decisions in human drivers, aiming to bridge the gap between naturalistic driving studies and laboratory-based cognitive science. While traditional gap acceptance models predict decision outcomes based on factors like distance and time-to-arrival (TTA), they offer little insight into the underlying cognitive processes. The authors hypothesize that dynamic evidence accumulation, a mechanism well-established in abstract perceptual tasks, also governs complex driving decisions. To test this, they developed a generalized drift-diffusion model that accounts for continuously varying sensory information and time pressure inherent in driving scenarios. The researchers conducted an experiment with 16 participants using a fixed-base driving simulator. Participants performed 120 left-turn trials each, where they had to decide whether to proceed ("go") or wait ("stay") when an oncoming vehicle appeared. The study employed a 3x3 factorial design, varying the initial distance (90, 120, or 150 m) and TTA (4, 5, or 6 s) of the oncoming vehicle. Data analysis focused on decision outcomes and response times for "go" decisions. Statistical analysis revealed that the probability of accepting a gap increased with both distance and TTA. Crucially, response times increased with TTA but were unaffected by distance, suggesting that time pressure, rather than spatial gap size, drives the speed of the decision. To model these findings, the authors adapted the classical drift-diffusion model by introducing two key modifications: a time-varying drift rate dependent on the linear combination of distance and TTA, and collapsing decision boundaries that decrease as TTA shrinks, reflecting increasing urgency. The model was fitted to individual and group-averaged data. It successfully explained the observed decision probabilities and response time distributions, capturing substantial individual differences. Cross-validation demonstrated that the model generalized to out-of-sample conditions, predicting behavior in held-out scenarios with high accuracy. Comparisons with simpler models showed that both the time-varying drift rate and collapsing boundaries were essential to capture the relationship between TTA and response time. The study concludes that dynamic evidence accumulation is a fundamental mechanism for left-turn gap acceptance, validating the application of cognitive process models to complex, real-world driving tasks. By modeling the decision process rather than just the outcome, this approach offers deeper insights into driver behavior. The findings have significant implications for the development of automated vehicles, enabling more accurate real-time prediction of human driver behavior and the simulation of realistic human-like interactions in virtual environments. This work represents a methodologically rigorous step toward integrating cognitive neuroscience principles into transportation engineering.
Key finding
A generalized drift-diffusion model with time-varying evidence accumulation and collapsing boundaries accurately explains and predicts human drivers' left-turn gap acceptance decisions and response times.
Methodology
simulator
Sample size: 16
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 openalex_abstract on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- gap acceptance
- mental model of traffic
- anticipation
- risk taking
- decision making risk perception
- situational awareness
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).
- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model, theory or model