2022 CDA Annual Report
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Summary
The 2022 Cooperative Driving Automation (CDA) Annual Report, published by the Federal Highway Administration (FHWA), documents the program’s achievements in advancing cooperative, safe, and efficient transportation systems. The report addresses the challenge of integrating connected and automated vehicles (CAVs) with infrastructure to improve traffic safety, throughput, and energy efficiency. It focuses on eight key use cases developed and tested in 2022, alongside broader program activities, stakeholder engagement, and software development. The research aims to establish baseline technologies and standard practices to facilitate the deployment of CDA technologies, emphasizing collaboration among government, industry, and academia. The study utilized the FHWA’s CARMA (Connected Automated Vehicle Research and Mobility Applications) ecosystem, including the CARMA Platform and V2X Hub, to conduct simulations and physical testing at the Turner-Fairbank Highway Research Center (TFHRC) and the Saxton Transportation Operations Laboratory (STOL). The primary methods involved simulation experiments in microscopic traffic simulators followed by proof-of-concept (PoC) testing, including integration, verification, and validation phases. Testing employed dedicated short-range communication (DSRC) and involved C-ADS-equipped vehicles interacting with roadside equipment. The report details specific use cases, including stop-controlled intersections, fixed-time traffic signals, cooperative perception, basic travel, work zone management, traffic incident management, road weather management, and port drayage. Key findings from the stop-controlled intersection use case demonstrated that CDA algorithms improved throughput, reduced average delay, and minimized fuel consumption by coordinating vehicle movements based on critical time step estimation and trajectory smoothing. Verification and validation tests confirmed that the system met acceptance criteria for safety, mobility, and trajectory smoothness, though localization issues occasionally caused unsuccessful runs. The fixed-time traffic signal use case showed that providing signal phase and timing (SPaT) messages to vehicles allowed for trajectory smoothing, reducing stop-and-go traffic and improving energy efficiency. Across all use cases, the research identified that while current implementations are viable for PoC testing, further development is needed to address mixed traffic environments, vulnerable road users, and V2V communication limitations. The significance of this work lies in its contribution to the standardization and deployment of CDA technologies. The report highlights that successful integration requires aggressive collaboration, accessible testing processes, and resolution of challenges such as GPS limitations in rural areas. By demonstrating the benefits of CDA in diverse scenarios, the FHWA establishes a foundation for future research and deployment. The report also notes the transition from DSRC to cellular vehicle-to-everything (C-V2X) and the importance of open-source software releases to foster industry adoption. Ultimately, the findings support the FHWA’s mission to enhance transportation safety and efficiency through cooperative automation, providing a roadmap for next-generation infrastructure and vehicle capabilities.
Key finding
The 2022 CDA program successfully demonstrated and validated multiple cooperative driving automation use cases, showing improved traffic metrics and safety through infrastructure-vehicle cooperation.
Methodology
mixed_methods
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 (28 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 | 25 | 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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