Response to Emergency Vehicles When Driving in a Mixed Vehicle Fleet
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Summary
This study investigates how drivers respond to approaching emergency vehicles within a mixed fleet of connected-automated vehicles (CAVs) and nonconnected vehicles. The research addresses a critical gap in traffic incident management: while CAVs theoretically allow for faster emergency response through vehicle-to-vehicle (V2V) communication, little is known about how human drivers behave when surrounded by varying levels of CAV market penetration. The authors hypothesized that in-vehicle alerts from connected emergency vehicles could improve driver response times and safety, particularly when other vehicles in the traffic stream also react to these alerts. To test this, researchers conducted a driving simulator experiment with 96 participants from the Washington, DC, metropolitan area. The study employed a between-subjects design manipulating three variables: vehicle automation (manual vs. SAE Level 2), vehicle connectivity (presence or absence of an in-vehicle CV alert), and CAV market penetration (no, low, or full penetration of surrounding traffic responding to alerts). Participants drove a simulated semiurban road where they became part of a four-vehicle string. An emergency vehicle approached from behind, triggering an auditory siren and, for half the participants, a visual and audio alert on the center console. Eye-tracking systems and vehicle kinematics were used to measure pullover behavior, response latency, gaze distribution, and driving safety metrics. The results demonstrated that CV alerts significantly improved driver compliance. Participants receiving alerts were 3.5 times more likely to pull over than those without alerts. Furthermore, CV alerts reduced the distance traveled before slowing down, allowing drivers to yield sooner. The benefits of V2V communication were most pronounced in high market penetration scenarios; drivers in Level 2 vehicles with CV alerts in full penetration conditions reduced speeds significantly sooner than those in no-penetration conditions. However, drivers in Level 2 vehicles with alerts slowed down slightly later than those in manual vehicles with alerts. Eye-tracking data revealed that participants with CV alerts spent more time gazing at the center console and approximately 7% less time looking at the roadway during the emergency vehicle’s approach. Despite these distractions, no crashes occurred, and safety metrics such as following distance and lane variability remained stable. The findings suggest that V2V communication is an effective tool for enhancing emergency vehicle awareness and response efficiency. The study supports the potential for CAVs to facilitate faster incident response, particularly when market penetration is high. However, the slight delay in response for Level 2 automated vehicles and the increased console gaze indicate that human factors remain critical in mixed fleets. These insights are valuable for transportation agencies and traffic incident management responders aiming to integrate CAV technologies into existing road networks safely and effectively.
Key finding
Connected vehicle alerts significantly improved drivers' emergency response behavior by increasing pullover rates and reducing the distance required to slow down, with the greatest latency benefits observed in full market penetration scenarios.
Methodology
simulator
Sample size: 96
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 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| 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 | 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
- Methodological Resource: tool software