Connected Vehicle Pilot Deployment Program Phase 3, Understanding and Enabling Cooperative Driving for Advanced Connected Vehicles in New York City – New York City Department of Transportation (NYCDOT)
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Summary
This report, produced by the New York City Department of Transportation (NYCDOT) as part of the USDOT Connected Vehicle Pilot Deployment Program Phase 3, addresses the applicability of Cooperative Driving for Advanced Connected Vehicles (CD for ACV) in urban environments. The research is motivated by the need to improve traffic safety and operational efficiency in New York City, particularly for vulnerable road users like pedestrians and bicyclists, while acknowledging that a fully automated vehicle environment is not yet feasible. The study aims to identify how existing infrastructure and data can support cooperative driving technologies, which rely on Vehicle-to-Everything (V2X) communication to coordinate actions among vehicles, infrastructure, and road users. The methodology involves a comprehensive literature review of domestic and international cooperative driving projects, including the FHWA’s Cooperative Automation Research Mobility Applications (CARMA) platform. The authors mapped three specific, testable use cases relevant to NYC: pedestrian and bicyclist safety through cooperation, cooperative work zones, and cooperative intersection management. To evaluate these use cases, the report conducts a data-driven analysis of existing agency-owned and third-party data sources. This includes assessing the reliability, applicability, and appropriateness of various data types, such as Basic Safety Messages (BSM), Signal Phase and Timing (SPaT), and traditional traffic data. A key component of the experimental design was the evaluation of thermal-based pedestrian detection technology already instrumented by NYCDOT at multiple signalized intersections with varying pedestrian densities. The findings identify specific infrastructure and data needs for each use case. For pedestrian safety, the analysis of thermal camera data provided insights into detection accuracy, latency, and counting capabilities at high- and low-density sites. The report details the data requirements for cooperative work zones, such as lane change harmonization and worker safety, and for intersection management, including conflict avoidance and signal timing optimization. It also assesses the availability of NYC WiFi hubs, 5G network coverage, and other Cooperative Automated Transportation (CAT) data. The study concludes that while significant data exists, gaps remain in data fusion, message redundancy, and the integration of non-connected entities. The significance of this work lies in its practical roadmap for implementing CD for ACV in a dense urban setting. The report provides detailed recommendations for future needs, including the development of a co-simulation environment for testing and the utilization of existing data to increase situation awareness. It highlights challenges related to security, privacy, and data trustworthiness. By mapping specific use cases to available NYC infrastructure and data, the report offers a framework for transportation agencies to advance shared perception and cooperative driving technologies, supporting the broader goals of Vision Zero and improved mobility without requiring 100% penetration of automated vehicles.
Key finding
The study identifies three primary cooperative driving use cases for New York City and evaluates the suitability of existing thermal pedestrian detection and other data sources to support them.
Methodology
review
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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