Analysis of the distraction impact on driving performance across driving styles: A driving simulator study in various speed conditions
DOI: 10.1371/journal.pone.0336480
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Summary
This study investigates how mobile phone distracted driving (MPDD) affects driving performance across different driving styles and traffic conditions. Motivated by the rising global prevalence of distracted driving and the lack of research combining driver style classification with distraction analysis, the authors examined the specific impacts of hands-free (HF) conversations (cognitive distraction) and texting (manual distraction). The research aimed to determine how these distractions influence lateral vehicle control, acceleration reaction time, and braking response, particularly when accounting for aggressive, moderate, and conservative driving behaviors. The researchers conducted a driving simulator study with 40 licensed participants (aged 18–42) using a medium-fidelity simulator. Participants performed car-following tasks under six traffic conditions: free-flow, coherent moving flow, synchronized flow, jam density, recovery from jam density, and collision avoidance. Each participant completed three randomized experimental conditions: baseline (control), HF conversation, and texting. During the distraction tasks, participants solved arithmetic problems either verbally or via text. Driving data were collected at 50 Hz, capturing metrics such as speed, acceleration, steering angle, and lane deviation. Participants were classified into driving styles (aggressive, moderate, conservative) using k-means clustering based on baseline performance in steady-state traffic conditions. Statistical analyses, including t-tests, Wilcoxon Signed-Rank Tests, and Friedman Tests, were used to evaluate performance metrics: Standard Deviation of Lateral Position (SDLP), Acceleration Reaction Time (ART), and Time to Initial Braking Location (TIBL). The findings revealed distinct effects based on distraction type and driving style. HF conversation generally stabilized lateral control, significantly reducing SDLP for moderate and conservative drivers in coherent, synchronized, and jam-density conditions. However, it decreased ART for conservative drivers and significantly increased TIBL for both moderate and conservative groups, indicating delayed braking responses. Conversely, texting impaired lateral control, significantly increasing SDLP for moderate and conservative drivers in free-flow conditions and for moderate drivers in coherent moving flow. Texting also significantly increased TIBL for moderate and conservative participants but had no significant effect on ART. Aggressive drivers showed fewer significant changes in SDLP compared to other groups. The study concludes that MPDD impacts driving performance differently depending on the distraction type and the driver’s inherent style. While HF conversations may induce more cautious lateral behavior, they impair reaction times, particularly for conservative drivers. Texting poses a greater risk to lateral stability and braking response for moderate and conservative drivers. These results highlight the need for nuanced safety policies and countermeasures that account for individual driving behaviors and specific distraction types, rather than treating all distracted driving incidents uniformly.
Key finding
Distraction effects on lateral control (SDLP) and braking timing (TIBL) depend on both traffic regime and driving style: texting hurts conservative/moderate drivers most in free flow, and both texting and HF conversation delay collision-avoidance braking.
Methodology
simulator
Sample size: N=40 drivers in driving simulator
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 canonical_url on 2026-05-03 (9 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | unpaywall | — | — | 3 | 2026-05-03 |
| 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 | openalex | — | — | 2 | 2026-06-01 |
| promote | success | — | — | — | 1 | 2026-05-03 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 18 | 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, observational prevalence
- Theoretical Contribution: conceptual framework