Effects of Cognitive Distraction and Driving Environment Complexity on Adaptive Cruise Control Use and Its Impact on Driving Performance: A Simulator Study
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This simulator study investigates how driver cognitive state and driving environment complexity influence the use of Adaptive Cruise Control (ACC) and how ACC usage subsequently affects driving performance. Motivated by the need to understand dynamic human-automation interaction in SAE Level 2 vehicles, the researchers aimed to determine if ACC reliance varies with environmental complexity or cognitive distraction, and whether such reliance improves or compromises safety metrics like speed compliance and lateral control. The experiment involved 29 licensed participants who completed six driving scenarios in a high-fidelity simulator using the CARLA engine. The study employed a within-subjects design varying two factors: driving environment complexity (low, medium, and high traffic density, with the highest level including road construction zones) and cognitive distraction (presence or absence of a secondary mental arithmetic task). Participants were instructed to adhere to speed limits (50 km/h in urban areas, 90 km/h on highways, 70 km/h in construction zones) and were free to activate or deactivate ACC at will. Driving performance was measured via speed limit adherence (analyzed with 0% and 5% error margins), standard deviation of lateral position (SDLP), and lane change frequency. Results indicated that driving environment complexity significantly influenced ACC usage, while cognitive distraction did not. Specifically, ACC engagement time was significantly lower in the most complex driving environment (Level 3) compared to the least complex (Level 1), though the number of ACC activations remained unaffected by either factor. Regarding driving performance, ACC usage had no significant effect on the number of lane changes. However, ACC engagement improved lateral vehicle control, as evidenced by reduced SDLP. Speed limit compliance depended on the measurement threshold: with a strict 0% margin, ACC engagement resulted in lower compliance due to minor speed fluctuations; however, with a 5% margin of error, ACC engagement significantly improved speed limit adherence compared to manual driving. The findings suggest that drivers dynamically adjust automation reliance based on environmental demands rather than internal cognitive load, reducing ACC use in complex traffic scenarios. The study highlights that ACC can enhance lateral stability and speed compliance when minor operational variances are accounted for. These results support the development of adaptive automation systems that consider environmental complexity to optimize driver assistance, ensuring automation is engaged when it benefits performance without compromising safety or driver involvement.
Key finding
Driving environment complexity significantly reduced adaptive cruise control engagement time, while ACC use improved lateral vehicle control without affecting lane change frequency.
Methodology
simulator
Sample size: 29
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 author_normalize on 2026-05-28.
| 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 | normalization | — | — | 2 | 2026-05-28 |
| 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 | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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: computational model