Cooperative Automation Research: High-Level Framework of CARMA Proof-of-Concept TSMO Use Case Testing for CARMA Streets

Ghiasi, Amir; Goforth, Wade; Hale, David; Huang, Zhitong; Racha, Sujith; Nallamothu, Sudhakar · 2022 · ROSA P / United States. Federal Highway Administration

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Summary

This report presents a high-level framework and Concept of Operations (ConOps) for CARMA Streets, a component of the Federal Highway Administration’s (FHWA) Cooperative Automation Research (CARMA) initiative. The research addresses the need to improve Transportation Systems Management and Operations (TSMO) by leveraging Cooperative Driving Automation (CDA). While previous CARMA iterations focused on vehicle-to-vehicle coordination like platooning, this work expands the ecosystem to include infrastructure-to-vehicle communication. The primary motivation is to enhance network safety, traffic throughput, and energy efficiency by enabling coordinated movement among vehicles and roadside infrastructure, particularly at conflict areas such as intersections. The methodology involves defining a system architecture for CARMA Streets, which functions as an edge-computing unit and interface for roadside equipment. This approach distributes computational burden away from centralized units or individual vehicles, allowing for real-time optimization of traffic flow. The report analyzes current arterial management limitations, such as stop-and-go traffic and shock wave propagation, and proposes CARMA Streets as a solution that determines optimal vehicle departure sequences. The framework is structured around ten specific operational use cases, including unsignalized conflict areas, signalized intersection optimizations, transit priority, incident management, work zones, weather management, and automated port drayage. The document also incorporates stakeholder feedback from transportation users and infrastructure owners to define functional requirements and performance metrics. Key findings outline the specific capabilities and benefits of the proposed system. CARMA Streets facilitates various CDA features, ranging from status sharing (e.g., signal phase and timing messages) to prescriptive control (e.g., coordinated trajectory smoothing). The report identifies that edge computing is superior to purely decentralized or centralized control schemes for real-time applications due to its balance of computational efficiency and system-wide optimization. The framework details how CARMA Streets supports different SAE cooperation classes, allowing for interactions with human-driven, connected, and fully automated vehicles. Expected benefits include reduced travel delay, improved travel-time reliability, lower energy consumption, and enhanced safety through better coordination at intersections. The significance of this work lies in its provision of a standardized, open-source framework for testing and deploying CDA technologies in real-world environments. By defining clear operational scenarios and functional requirements, the report guides the development of interoperable systems that can integrate with existing intelligent transportation systems. It serves as a foundational document for researchers, developers, and policymakers aiming to accelerate the market readiness of cooperative automation. The establishment of this framework supports the broader goal of transitioning transportation networks toward more efficient, safe, and sustainable operations through the integration of automated driving systems and connected infrastructure.

Key finding

The report establishes a conceptual framework for CARMA Streets that defines ten specific operational use cases and functional requirements to enable cooperative driving automation for improved traffic safety and efficiency.

Methodology

review

Sample size: 100

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• v2x connected vehicle
• ehmi external hmi

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• Methodological Resource: dataset resource