Factors influencing visual search in complex driving environments.
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study investigates how specific roadway environment factors influence drivers' perceived complexity, aiming to inform roadway design and safety guidance. Motivated by the critical role of driver perception in traffic safety and the prevalence of human error in crashes, the research seeks to identify environmental characteristics that contribute to cognitive load. The study developed descriptive and predictive models of perceived complexity across static and dynamic environments, incorporating diverse demographic groups, including novice drivers, to understand how experience and environment interact. The methodology comprised two primary experiments. The static study utilized 100 unique images (75 on-road photographs and 25 simulator-generated images) presented to 288 participants from high schools, universities, and a public festival. Participants rated the "task complexity" (difficulty of driving) and "visual complexity" (appearance) of each image. The dynamic study involved participants rating the complexity of short driving simulator videos. Researchers employed factor analysis to classify roadway characteristics and linear regression to predict perceived complexity based on these factors and driver demographics. Key findings from the static experiment revealed that environmental conditions (e.g., weather, lighting), urban arterial characteristics, and roadside restrictions significantly increased perceived complexity. Simulator images were consistently rated as less complex than on-road images, suggesting a potential bias in simulator fidelity. Regarding demographics, drivers with less than 12 months of licensure rated environments as less complex than experienced drivers, though this finding is confounded by age. Younger drivers were particularly sensitive to the openness of non-urban environments, potentially underestimating associated risks. In the dynamic experiment, traffic volume had the greatest effect on perceived complexity. Additionally, work zones using drums (lower path guidance) interacted with lane configurations and roadside objects to increase complexity more than those using portable concrete barriers. The study also found that the effects of multiple factors were not additive, indicating complex interactions between environmental elements. The significance of these findings lies in their application to transportation engineering and safety. The results suggest that simulator studies may need to adjust scenarios to match the perceived complexity of real-world environments. The identification of high-complexity factors can guide road safety audits and inform the design of work zones to minimize perceptual shifts that lead to driver error. Furthermore, the findings highlight the need for targeted driver education for young drivers, emphasizing the challenges of rural and freeway conditions. Ultimately, the study provides a foundation for future research linking perceived complexity to specific performance metrics, such as lane deviation and cognitive workload, to enhance roadway safety.
Key finding
Traffic volume had the greatest effect on perceived complexity in dynamic environments, while environmental conditions, urban arterial settings, and roadside restrictions significantly increased perceived complexity in static environments.
Methodology
mixed_methods
Sample size: 288
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 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 3 | 2026-05-28 |
| 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 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 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.
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
- Methodological Resource: tool software
- Theoretical Contribution: theory or model