FHWA Cooperative Automation Research: CARMA Proof-of-Concept TSMO Use Case Testing: CARMA Cooperative Perception Low-Level Concept of Operations

Lou, Yingyan; Hale, David; McConnel, Michael; Ghiasi, Amir; Jannat, Mafruhatul; Racha, Sujith · 2022 · ROSA P / United States. Federal Highway Administration

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Summary

This report presents a low-level concept of operations (ConOps) for Cooperative Perception (CP) within the Federal Highway Administration’s (FHWA) Cooperative Driving Automation (CDA) Program, specifically focusing on the CARMA Ecosystem. The research addresses the need to enhance situational awareness for connected and automated vehicles (CAVs) by enabling the sharing of locally perceived road object data through vehicle-to-everything (V2X) communications. The primary motivation is to improve safety, traffic throughput, and energy efficiency, particularly in scenarios where line-of-sight is obstructed or perception capabilities are limited. This document serves as a technical reference for system developers, researchers, and infrastructure operators to define operational needs and functional requirements for CP integration. The study focuses on a specific use case: vulnerable road users (VRUs), such as pedestrians, crossing in controlled conflict areas like intersections. It details three distinct situations involving a large vehicle (e.g., heavy or transit vehicle), a passenger vehicle equipped with cooperative automated driving systems (C-ADS), a VRU, and roadside infrastructure. Situation 1 involves both vehicles stopped at an intersection where infrastructure-based sensors detect a pedestrian clearing the crosswalk outside the designated phase, relaying this data to the obstructed passenger vehicle. Situation 2 adds complexity by having both the large vehicle and infrastructure share perception data, requiring the passenger vehicle to perform data fusion. Situation 3 involves a moving passenger vehicle approaching a stopped large vehicle, utilizing fused CP information for trajectory planning to avoid collisions. The analysis covers user-oriented operational descriptions, stakeholder perspectives, and specific performance metrics for each scenario. The findings define the system framework and requirements necessary to support these CP scenarios within the CARMA Ecosystem, which includes CARMA Platform, Messenger, Streets, and Cloud. The report identifies five key technical considerations for CP: enhanced object detection and perception, communication protocols, cybersecurity, data fusion algorithms, and the effective application of shared information. It establishes that CP can significantly mitigate risks in edge cases, such as when a pedestrian is obscured by a large vehicle, by providing redundant perception data that reduces uncertainty. The document outlines specific operational user needs and system functional requirements, such as the ability for roadside equipment to generate object-based CP messages and for CAVs to integrate this data into their local object lists and motion planning algorithms. The significance of this work lies in its contribution to the maturation of cooperative automation technologies for real-world deployment. By detailing the low-level operational requirements for CP, the report provides a roadmap for algorithm development and implementation within the open-source CARMA Ecosystem. It demonstrates how cooperative perception can bridge gaps in individual vehicle sensing, thereby enhancing safety for vulnerable road users and enabling more effective transportation systems management and operations (TSMO) strategies. This ConOps supports the broader FHWA goal of accelerating the market readiness of CDA technologies by providing standardized, evidence-based frameworks for testing and integration.

Key finding

The document establishes the system framework and functional requirements for cooperative perception to enhance situational awareness and safety for vulnerable road users in controlled conflict areas.

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 (6 acquisition events logged).

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

Topics

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• situational awareness
• v2x connected vehicle
• ehmi external hmi
• vru facing ehmi