Cooperative Automation Research: CARMA Proof-of-Concept Transportation System Management and Operations Use Case 1 - Basic Arterial Travel–Stop-Controlled Intersections

Soleimaniamiri, Saeid; Li, Xiaopeng (Shaw); Yao, Handong; Ghiasi, Amir; Vadakpat, Govind; Bujanovic, Pavle; Lochrane, Taylor; Stark, John; Blizzard, Katherine; Hale, David · 2021 · ROSA P / United States. Federal Highway Administration

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Summary

This report presents a Concept of Operations (ConOps) for Transportation Systems Management and Operations (TSMO) Use Case 1, focusing on basic arterial travel at stop-controlled intersections. Developed by the Federal Highway Administration (FHWA) as part of the Cooperative Automation Research (CARMA) initiative, the document addresses the need to improve safety, traffic throughput, and energy efficiency at unsignalized intersections. The research is motivated by the limitations of existing cooperative driving automation (CDA) studies, which often rely on computationally intensive centralized control or decentralized methods that fail to maximize system-wide benefits. Furthermore, prior research has largely ignored the impact of varying levels of vehicle cooperation capabilities as defined by the SAE J3216 standard. The proposed solution utilizes an edge-computing-based cooperative control framework integrated into the CARMA3 platform. This approach distributes computational burden by assigning high-level scheduling decisions to roadside equipment (RSE) while delegating low-level trajectory control and collision avoidance to individual cooperative automated driving system (C–ADS) equipped vehicles. The system relies on vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) communications using Dedicated Short-Range Communications (DSRC). Key technical components include Critical Time Step Estimation (CTSE) to determine optimal entry times and Trajectory Smoothing (TS) to optimize vehicle speeds. The design explicitly accounts for different SAE cooperation classes, ranging from status sharing to prescriptive commands, allowing the system to function with varying degrees of vehicle automation and connectivity. The report outlines the operational requirements, infrastructure needs, and performance metrics for this use case. It defines specific functional requirements for both vehicles and infrastructure, including the exchange of state information and negotiation results. The analysis highlights potential benefits such as reduced stop-and-go traffic, minimized travel delay, and lower energy consumption and emissions. By coordinating vehicle movements to allow conflict-free simultaneous passage rather than sequential single-vehicle entry, the system aims to increase intersection throughput. The document also identifies disadvantages and limitations, noting that the effectiveness of the system depends on the penetration rate of C–ADS vehicles and the reliability of communication links. The significance of this work lies in its contribution to the CARMA ecosystem by extending cooperative automation strategies from highway platooning to complex arterial intersections. It provides a foundational framework for developers and researchers to implement CDA technologies that balance centralized scheduling efficiency with decentralized vehicle control. By addressing the gap in real-world testing for stop-controlled intersections and incorporating standardized cooperation classes, this ConOps supports the advancement of automated driving systems toward improved network efficiency and safety. The report serves as a technical guide for stakeholders, including system developers and infrastructure operators, to understand the operational concepts and requirements necessary for deploying cooperative automation at unsignalized intersections.

Key finding

The study proposes a cooperative control framework for stop-controlled intersections that combines vehicle coordination with trajectory optimization to increase throughput and reduce energy consumption while ensuring safety.

Methodology

theoretical

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 (46 acquisition events logged).

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

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• v2x connected vehicle