FHWA Cooperative Automation Research: CARMA Proof-of-Concept Transportation System Management and Operations Use Case 3 – Traffic Signal Optimization With CDA at Signalized Intersections

Soleimaniamiri, Saeid; Li, Xiaopeng (Shaw); Yao, Handong; Ghiasi, Amir; Vadakpat, Govind; Bujanovic, Pavle; Lochrane, Taylor; Stark, John; Racha, Sujith; Hale, David · 2022 · 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 3, focusing on traffic signal optimization with cooperative driving automation (CDA) at signalized intersections. Developed by the Federal Highway Administration (FHWA) as part of the CARMA initiative, the project aims to enhance the CARMA ecosystem by enabling coordinated movement between vehicles and roadside infrastructure. The primary motivation is to address limitations in existing control strategies, which often rely on computationally intensive centralized schemes or limited decentralized approaches that fail to maximize network efficiency. The study seeks to reduce traffic congestion, improve energy efficiency, and increase infrastructure throughput by leveraging vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) communications. The proposed solution utilizes an edge-computing-based cooperative control framework designed for cooperative automated driving system (C-ADS) equipped vehicles. The approach consists of three main components: signal optimization (SO) for roadside equipment, critical time step estimation (CTSE) for infrastructure, and trajectory smoothing for vehicles. This framework distributes computational burdens by having infrastructure handle high-level scheduling decisions while individual vehicles manage low-level trajectory control and collision avoidance. The design accounts for varying levels of vehicle automation and cooperation classes defined by the SAE J3216 standard, ensuring compatibility across different vehicle capabilities. The report details the technological framework, infrastructure configuration needs, and performance metrics, including vehicle behavior and traffic performance indicators. It also outlines operational scenarios and a validation plan involving simulation and field testing to assess the system's effectiveness. The findings indicate that this cooperative control framework offers significant advantages over traditional methods by reducing operational complexity and liability for traffic operators while maintaining real-time applicability. By smoothing vehicle trajectories and optimizing signal phases, the system aims to eliminate stop-and-go traffic, reduce backward shock-wave propagation, and minimize fuel consumption and emissions. The report highlights that existing studies often assume uniform cooperation behaviors or focus solely on single-vehicle trajectories, whereas this ConOps addresses mixed-traffic scenarios and varying cooperation classes. The proposed system allows for simultaneous optimization of signal timing and vehicle trajectories, potentially increasing intersection throughput and travel-time reliability. The significance of this work lies in its contribution to the development of scalable, real-time CDA applications for surface arterials. By providing a detailed operational concept and validation plan, the report serves as a guide for system developers, researchers, and stakeholders in implementing cooperative automation strategies. It bridges the gap between theoretical optimization models and practical deployment by addressing computational constraints and diverse vehicle capabilities. The outcomes support the broader goal of improving safety, mobility, and environmental sustainability in transportation networks through integrated vehicle-infrastructure cooperation.

Key finding

The report defines a cooperative control framework for traffic signal optimization and trajectory smoothing at signalized intersections but does not present empirical performance results.

Methodology

other

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 (7 acquisition events logged).

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

Topics

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• v2x connected vehicle