Emergency Vehicle-to-Vehicle Communication: Final Report
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Summary
This report addresses the critical safety and efficiency challenges faced by emergency response vehicles (ERVs) navigating congested traffic. Motivated by high rates of ERV-related crashes and the limitations of traditional siren-based warning systems, the study investigates how vehicle-to-vehicle (V2V) communication can facilitate safer and faster ERV travel. The research aims to determine the potential benefits of V2V technology, identify optimal conditions for its use, define appropriate non-ERV behaviors, and develop a functional communication prototype. The study employed a multi-method approach comprising micro-simulation, field testing, and optimization modeling. First, researchers used VISSIM software with a Car2x API extension to simulate a network based on the Northern Virginia Connected Vehicle Test Bed. Twenty-three experiments varied factors such as traffic volume, signal cycle length, speed distributions, and the presence of V2V communication and signal preemption to assess their impact on ERV travel time. Second, a V2V communication prototype was developed and field-tested with twelve drivers aged 25 to 50. The prototype alerted drivers via infotainment system flashes and audible instructions to move left, right, or stay put, allowing researchers to measure reaction times. Third, a mixed-integer nonlinear program (MINLP) optimization model was formulated to maximize ERV forward progress by assigning specific movement instructions to non-ERVs within a road segment. The findings indicate that V2V communication significantly reduces ERV travel time in congested conditions compared to baseline scenarios without communication. The field tests revealed that driver reaction times to the V2V instructions varied between 1.4 and 5.8 seconds, providing empirical data on human response capabilities. The optimization model demonstrated its capability to effectively coordinate non-ERV behavior, assigning vehicles to specific locations out of the ERV’s path to maximize the ERV’s speed in uniform roadway sections. The simulation results further highlighted that while signal preemption aids ERV movement, V2V communication offers a complementary mechanism for managing traffic flow and driver awareness. The significance of this work lies in its contribution to the development of advanced warning systems for emergency services. By demonstrating that V2V communication can reduce travel times and improve safety through cooperative driving behaviors, the study supports the integration of connected vehicle technologies into emergency response protocols. The research provides a foundational framework for optimizing ERV navigation, suggesting that precise, directional instructions via V2V can overcome the ambiguities of traditional sirens. This approach has the potential to reduce crash rates involving ERVs and non-ERVs, thereby enhancing public safety and operational efficiency for emergency responders.
Key finding
V2V communication reduced ERV travel time in congested traffic, and driver reaction times to V2V movement instructions ranged from 1.4 to 5.8 seconds.
Methodology
mixed_methods
Sample size: 12
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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Information type
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- Empirical Findings: behavioral performance data
- Theoretical Contribution: computational model