Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-22-064 SEPTEMBER 2022

Concept of Operations for CARMA 

Integrated Highway Prototype 2 
FOREWORD

The Federal Highway Administration’s (FHWA’s) cooperative driving automation (CDA) 

program, formerly known as the CARMASM program, is an initiative to enable collaboration for 

research and development of CDA technologies. The CDA program develops and maintains an 

ecosystem of open-source software tools, which are known as the CARMA Ecosystem, to enable 

CDA research. The CARMA Ecosystem is a research environment that enables communication 

between vehicles and roadside infrastructure devices to support coordinated movement to 

improve safety, traffic throughput, and energy efficiency of the transportation network. 

In 2015, FHWA’s Office of Operations Research and Development developed a cooperative 

adaptive cruise control proof-of-concept prototype that was installed in five research vehicles. 

From there, the CARMA Ecosystem further evolved through testing and integration. At the time 

of this writing, the CDA program is advancing into automated driving systems that leverage 

infrastructure to support cooperative automation strategies. This project expands previous 

functionality to include three key feature groups on freeways—cooperative lane follow 

(platooning and cooperative adaptive cruise control), cooperative lane coordination (cooperative 

lane change, merge, and weave), and cooperative traffic management (speed and gap control, 

lane assignment, and queue management). The intended audience for this report is CDA 

stakeholders, such as system developers, analysts, researchers, application developers, and 

infrastructure owners and operators. 

Brian P. Cronin, P.E. 

Director, Office of Safety and Operations 

Research and Development

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 

Recommended citation: Federal Highway Administration, Concept of Operations for CARMA 

Integrated Highway Prototype 2 (Washington, DC: 2022) https://doi.org/10.21949/1521866.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-22-064

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Concept of Operations for CARMA Integrated Highway 

Prototype 2 

5. Report Date

September 2022

6. Performing Organization Code

7. Author(s)

Jiaqi Ma (ORCID: 0000-0002-8184-5157), Edward Leslie

(ORCID: 0000-0002-1711-2661), Amir Ghiasi (ORCID: 

0000-0002-0986-9840), Kyle Rush (ORCID: 0000-0001-

9769-9273) 

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

Leidos, Inc.

11251 Roger Bacon Drive

Reston, VA 20190

University of Cincinnati

765 Baldwin Hall

Cincinnati OH 45221-0071

11. Contract or Grant No.

DTFH6116D00030-693JJ319F000369

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period Covered

Concept of Operations; September 2020–June 

2021

14. Sponsoring Agency Code

HRDO-10 

15. Supplementary Notes

The Government Task Manager was Pavle Bujanovic(HRSO-40; ORCID: 0000-0001-6589-3207). (Original 

task manager was Mohammed Yousuf.) 

16. Abstract

Cooperative driving automation aims to improve the safety and flow of traffic and facilitate road operations by 

supporting the movement of multiple vehicles in proximity to one another. This outcome is accomplished by 

sharing information that can be used to influence dynamic driving tasks directly or indirectly by one or more 

nearby road users. Vehicles and infrastructure elements engaged in cooperative driving automation may share 

information, such as state (e.g., vehicle position, signal phase) or intent (e.g., planned vehicle trajectory, signal 

timing), or they may seek agreement on a plan (e.g., coordinated merge). Cooperation among multiple 

participants in traffic can improve safety, mobility, situational awareness, and operations. For Integrated 

Highway Prototype 2 (IHP2), three key feature groups included cooperative lane follow (platooning and 

cooperative adaptive cruise control), cooperative lane coordination (cooperative lane change, merge, and 

weave), and cooperative traffic management (speed and gap control, lane assignment, and queue management).

The combination of these feature groups to form IHP2 provides benefits to both the individual traveler and the 

overall traffic system.

17. Key Words

Integrated highway prototype, cooperative driving 

automation, CARMA3, speed harmonization, vehicle 

platooning, cooperative merge 

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161

http://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

50

22. Price

n/a

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized. 
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iii

TABLE OF CONTENTS

CHAPTER 1. SCOPE AND SUMMARY ................................................................................... 1

Identification ........................................................................................................................... 1

Document Overview................................................................................................................ 1

Background ......................................................................................................................... 1

Objective ............................................................................................................................. 2

Audience ............................................................................................................................. 3

Document Structure ............................................................................................................ 3

CHAPTER 2. CURRENT SITUATION AND OPPORTUNITIES FOR CHANGES ........... 5

Existing IHP Features............................................................................................................. 5

IHP1 Background and Scope .............................................................................................. 5

CACC and Platooning......................................................................................................... 6

Cooperative Merge.............................................................................................................. 6

Speed Harmonization .......................................................................................................... 8

IHP1 Infrastructure ........................................................................................................... 10

IHP Stakeholders .................................................................................................................. 11

Road Users ........................................................................................................................ 11

IOOs .................................................................................................................................. 12

Justification for, and Nature of, Changes ........................................................................... 13

CHAPTER 3. INTEGRATED HIGHWAY PROTOTYPE 2 OPERATIONAL 

CONCEPT ................................................................................................................................... 15

CDA ........................................................................................................................................ 15

SAE International J3216 ................................................................................................... 15

CARMA3 .......................................................................................................................... 17

Technological Framework for IHP2 ................................................................................... 18

CLF ................................................................................................................................... 21

CLC ................................................................................................................................... 22

CTM .................................................................................................................................. 26

Infrastructure Configuration and Needs ............................................................................ 28

Summary of IHP2 Needs ...................................................................................................... 30

Performance Metrics ............................................................................................................ 32

Performance Metrics for Vehicle Behavior ...................................................................... 32

Performance Metrics for Traffic Performance .................................................................. 33

CHAPTER 4. OPERATIONAL SCENARIOS ........................................................................ 35

End-to-End CDA Operations From Freeway Entering to Exiting .................................. 35

CTM with Lane Assignment and Speed Control ............................................................... 37

Dedicated Facility Operations for Early Deployment ....................................................... 38

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM ................................................. 41

Summary of Potential Benefits and Opportunities ............................................................ 41

System Validation Plan......................................................................................................... 41

Simulation Testing ............................................................................................................ 41

Field Testing ..................................................................................................................... 42

Summary of Impacts............................................................................................................. 42

REFERENCES ............................................................................................................................ 43 
iv

LIST OF FIGURES

Figure 1. Illustration. Vehicle platoon. ........................................................................................... 6

Figure 2. Illustrations. Comparison between regular human merge and cooperative merge. ........ 8

Figure 3. Illustration. Infrastructure-to-vehicle speed control algorithmic logic. ........................... 9

Figure 4. Illustration. CARMA planning modules. ...................................................................... 19

Figure 5. Illustration. IHP2 CLF. .................................................................................................. 22

Figure 6. Illustrations. Cooperative lane change. ......................................................................... 23

Figure 7. Illustrations. Cooperative merge. .................................................................................. 24

Figure 8. Illustrations. Cooperative weave. .................................................................................. 26

Figure 9. Charts. Projected trajectories with and without smoothing. .......................................... 27

Figure 10. Illustrations. End-to-end CDA operations. .................................................................. 37

Figure 11. Illustrations. Two dedicated facility operational scenarios. ........................................ 39

LIST OF TABLES

Table 1. Projects associated with this CARMA development effort. ............................................. 2

Table 2. Road user characteristics and needs................................................................................ 12

Table 3. Overview of SAE International cooperation classes and automation levels (SAE 

International 2020). .................................................................................................................. 16

Table 4. IHP2 features and descriptions. ...................................................................................... 20

Table 5. Differences between platooning and CACC. .................................................................. 21

Table 6. Infrastructure needs for road users and IOOs. ................................................................ 29

Table 7. Exchanges and rules between CDA vehicles and the cloud. .......................................... 30

Table 8. Vehicle-side operational needs for IHP2. ....................................................................... 30

Table 9. Traffic performance measures for IHP2 evaluation. ...................................................... 33
v 

LIST OF ACRONYMS

ACC adaptive cruise control 

ACK acknowledge 

ADS automated driving systems 

AMS analysis, modeling, and simulation 

CACC cooperative adaptive cruise control

C–ADS cooperative automated driving system 

Caltrans California Department of Transportation 

CAV connected automated vehicle

CDA cooperative driving automation 

CLC cooperative lane coordination 

CLF cooperative lane follow 

ConOps concept of operations 

CTM cooperative traffic management 

DDT dynamic driving task 

DOT department of transportation 

DSRC dedicated short-range communication

FHWA Federal Highway Administration

HOV high-occupancy vehicle 

HRSO Office of Safety and Operations Research and Development 

ID identification

IHP Integrated Highway Prototype 

IHP1 Integrated Highway Prototype 1 

IHP2 Integrated Highway Prototype 2 

IOO infrastructure owner and operator 

ITS intelligent transportation system 

N/A not applicable 

NACK not acknowledged

OBU onboard unit 

R&D research and development

RSU roadside unit 

SOV single-occupancy vehicle

STOL Saxton Transportation Operations Laboratory 

TCD traffic control device

TMC Traffic Management Center

TSMO Transportation Systems Management and Operations 

V2I vehicle-to-infrastructure 

V2V vehicle-to-vehicle

VSL variable speed limit

1 

CHAPTER 1. SCOPE AND SUMMARY

IDENTIFICATION

This document serves as a concept of operations (ConOps) for the basic travel use case to 

include SAE International® Level 3 and above automated driving systems (ADS), with and 

without connectivity and cooperation. The basic travel use case, as part of the Integrated 

Highway Prototype 2 (IHP2) project, includes cooperative lane follow (CLF), cooperative lane 

coordination (CLC), and cooperative traffic management (CTM) in a general-purpose freeway 

facility.

DOCUMENT OVERVIEW

Background 

The Office of Safety and Operations Research and Development (HRSO) performs 

transportation operations research and development (R&D) for the Federal Highway 

Administration (FHWA). Onsite R&D is conducted at the Saxton Transportation Operations 

Laboratory (STOL) established at Turner-Fairbank Highway Research Center. HRSO conducts 

safety and operations R&D based on a national perspective of the transportation needs of the 

United States.

In 2015, HRSO designed, built, and installed a cooperative adaptive cruise control (CACC) 

proof-of-concept prototype system in a fleet of five vehicles. The CACC system was built on 

CARMA PlatformSM as an advancement of standard adaptive cruise control (ACC) systems by 

using vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC) to automatically 

synchronize the longitudinal movements of many vehicles within a string. The CACC system 

was the first in the United States to demonstrate the capabilities of this technology with a 

five-vehicle CACC string. 

A subsequent task order developed a new reference platform, CARMA2SM, to enable research to 

be easily shared and integrated into industry research vehicles. The project advanced the CACC 

functionality and developed a proof-of-concept platooning application that enabled 

leader-follower behavior and allowed vehicles to begin to negotiate with one another. The 

project also developed the Integrated Highway Prototype 1 (IHP1), which integrated speed 

harmonization, lane change/merge, and platooning into one package. This research focused on 

developing the understanding around negotiations among entities and how this negotiation can 

be done efficiently to help improve traffic flow based on cooperative tactical maneuvers. 

A current task order is producing the third iteration of CARMASM. CARMA3SM takes the 

platform into the world of ADS with SAE Level 3, and above, automation. The approach takes 

advantage of an open-source ADS platform to enable ADS functionality to be used for 

cooperative automation strategies.

CARMA CloudSM, CARMA Messenger, and CARMA Streets are also being developed. 

CARMA Cloud represents the infrastructure piece of cooperative driving automation (CDA), 

where vehicles and other entities may communicate with infrastructure to increase the safety and 
2 

efficiency of the transportation network. CARMA Messenger represents the capability of 

moving, non-automated entities (e.g., first-responder vehicles, pedestrians, buses) to 

communicate with CARMA-equipped vehicles and infrastructure to improve the performance of 

the network. CARMA Streets enables vehicles to communicate with the infrastructure at 

intersections and provides an interface to traffic signal controllers to optimize travel through an 

intersection. CARMA Platform, CARMA Cloud, CARMA Messenger, and CARMA Streets are 

open source and are built with the goal of benefiting CDA research. Table 1 lists the projects 

associated with this development. 

Table 1. Projects associated with this CARMA development effort.

Task Order Product Title

STOL I T-13005 CARMA Development of a Platform Technology for 

Automated Vehicle Research

STOL II 0013 CARMA2 Development of Connected and Automated 

Vehicle Capabilities: Integrated Prototype I 

STOL II 693JJ318F000225 CARMA3 Development of Cooperative Automation 

Capabilities: Prototype II

STOL II 693JJ319F000360 CARMA TSMO Cooperative Automation Research: 

CARMA Proof-of-Concept TSMO Use 

Case Testing

TSMO = transportation systems management and operations.

Objective

The objective of this project, Automated Driving Systems (ADS) OEM-Industry Research 

Collaboration and Integrated Highway Prototype (IHP), is to advance the basic travel use case 

using the CARMA ecosystem to enable further interaction of CDA vehicles with the road 

infrastructure to enhance infrastructure, improve efficiency, and reduce traffic congestion 

through transportation systems management and operations (TSMO). 

This work builds on the research from the current CARMA3 project that developed CARMA 

Platform and CARMA Cloud. This project will focus on migrating the IHP on CARMA3 to 

enable further cooperative automation research, leading toward deployment. A team of CARMA 

users supported development and testing using their own CARMA-enabled vehicles.

The objective of this document is to describe the use of CDA as it applies to the basic travel use 

case in general freeway operations. CDA in the basic travel use case is focused around the ability 

to test and evaluate cooperative automation transportation strategies during recurring congestion 

on freeways. These transportation strategies/features, developed using automated driving 

technology and communications, tactically address two high-level objectives: reducing traffic 

congestion and increasing infrastructure efficiency. As part of the CARMA IHP2 project, this 

ConOps focuses on three categories of features: CLF (platooning, CACC), CLC (merge, weave, 

lane change), and CTM (speed control, lane assignment). 
3 

Audience

The intended audience for this document includes: 

• U.S. Department of Transportation connected automated vehicle (CAV) and cooperative 

automation program stakeholders.

• System developers who will create and support CDA algorithms based on the system 

concepts described in this document. 

• Managed lane owners and operators. 

• Analysts, researchers, and CDA application developers. 

Document Structure

The structure of this document is generally consistent with the outline of a system operational 

concept document described in annex A of ISO/IEC/IEEE Standard 29148:2011 (IEEE 2011). In 

U.S. transportation systems engineering practice, this structure is called a ConOps, and that title 

is included in this document. Some sections have been enhanced with more detailed content than 

that described in the standard, and titles of some sections may have been edited to capture those 

enhancements more specifically. 

• Chapter 1 defines the scope of the ConOps. 

• Chapter 2 reviews the concepts of IHP1 systems and identifies the need for changes from 

the current situation on highways. 

• Chapter 3 describes the concept for the new IHP2 system capabilities and their operations 

and presents a detailed description of operational concepts. 

• Chapter 4 describes IHP2 operational scenarios for managed lane operations. 

• Chapter 5 provides an analysis of the expected improvements, operational and research

impacts, a validation plan, disadvantages, and limitations.

5 

CHAPTER 2. CURRENT SITUATION AND OPPORTUNITIES FOR CHANGES

This chapter discusses the existing IHP, describes the functionalities of IHP1 (e.g., Level 1 

automation), and describes the limited infrastructure and operational scenarios used in the project 

and field testing. 

EXISTING IHP FEATURES

This section discusses IHP1 features on the vehicle and infrastructure sides. 

IHP1 Background and Scope

In 2017, STOL conducted research to develop a new reference platform, CARMA2, to enable 

research capabilities to be easily shared and integrated into industry research vehicles. This 

project advanced the CACC functionality and developed a proof-of-concept platooning 

application that enabled leader-follower behavior and allowed vehicles to begin to negotiate with 

one another within an existing platoon. This project also developed the IHP1, which integrated 

speed harmonization, cooperative merge, and platooning into one application for the first time.

The goal of IHP1 was to demonstrate the potential of relying on SAE Level 1 automation (i.e., 

longitudinal control, which is the control of throttle and braking) for early benefits of ADS and 

CDA deployments (SAE International 2020). All lateral maneuvers were still controlled by 

human drivers, which may reduce overall system efficiency due to the uncertainties of human 

behavior and safety considerations. IHP1 only addressed operational decisions in local traffic

(i.e., immediate operations in a freeway merge area). Tactical decisions that can further enhance

system performance on a larger scale (e.g., a freeway corridor), such as lane assignment and 

comprehensive platoon formation where vehicles begin in different lanes, were not part of IHP1.

From the infrastructure perspective, IHP1 featured two key assumptions. First, IHP1 assumed 

that roadside units (RSU), which are connected to a traffic management center (TMC), play a 

role in coordinating longitudinal maneuvers, such as setting traveling speeds by considering 

downstream traffic conditions and coordinating longitudinal movements of merging and 

mainline vehicles. For this assumption, IHP1 operations require 100 percent vehicle connectivity 

in the traffic stream, although human-driven and CDA vehicles may coexist in the traffic stream. 

The second assumption was that IHP1 was designed for a managed lane scenario with dedicated 

ramps as an early-deployment location. This assumption facilitated the IHP1 design to form 

high-performance traffic streams along freeway facilities (similar to a recent FHWA exploratory 

advanced research project) (Liu et al. 2018). As a result, CDA vehicles, whether on mainline 

managed lanes or onramps, are observed by, and receive commands from, the roadside 

infrastructure to ensure minimum disturbances to mainline traffic when the onramp vehicles 

merge into the mainline managed lane.

The operational concepts of the three IHP1 component features are discussed separately in the 

subsections that follow. This report does not aim to provide a comprehensive literature review of 

all academic studies on CDA. Instead, the focus of this report is on reviewing the operational 

concepts of each IHP1 feature and showing how each feature is integrated with others, with 

findings from the most relevant, well-recognized studies. 
6 

CACC and Platooning 

Adding V2V communications to an ACC system is one way to turn it into a CACC system. As a 

result, an ADS vehicle will leverage its ability to communicate with surrounding vehicles to 

maintain an optimal following distance. The ADS vehicle can slow down when it gets too close 

to another vehicle, speed up to maintain the desired headway, or communicate with other 

vehicles about speed/trajectory changes while in ACC mode. The primary motivation for the 

development of CACC is to reduce traffic congestion by improving highway capacity and 

throughput and attenuating traffic flow disturbances. The use of V2V communication could 

allow the mean following time gap to be reduced from about 1.4 s when the vehicle is driven

manually to approximately 0.6 s when CACC is used, resulting in an increase in highway lane 

capacity (Nowakowski et al. 2010). Several highway traffic simulations showed that autonomous 

ACC alone, even at high market penetration rates, had little effect on lane capacity (Nowakowski 

et al. 2010; Shladover, Su, and Lu 2012). On-the-road experiments showed that a stream of 

autonomous ACC vehicles is string unstable, resulting in a negative impact on lane capacity

(Milanés et al. 2014). However, with the shorter following gaps enabled by CACC systems, lane 

capacity could be increased from the typical 2,200 vehicles per hour to almost 4,000 vehicles per 

hour at 100 percent market penetration. See Wang, Wu, and Barth (2018) and Wang et al. (2020) 

for a review of existing efforts on CACC algorithms and limited testing. 

IHP1 adopts the concept of platooning, which gives special responsibilities to platoon leaders to 

coordinate platoon formation, dissolution, and rear-join for vehicles external to the platoon (e.g., 

a merge vehicle). This project advanced the CACC functionality and developed a 

proof-of-concept platooning application (referred to as all-predecessor following) that enabled 

leader-follower behavior and allowed vehicles to begin to negotiate with one another within an 

existing platoon (Bujanovic 2018). However, platooning in IHP1 can be further extended to 

enable a true hierarchical control structure, with the platoon leader coordinating platooning 

membership with vehicles within the platoon and on nearby lanes. Figure 1 illustrates a platoon 

in which the platoon leader controls the platoon size, intraplatoon gaps, and interplatoon gaps. 

Source: FHWA. 

Figure 1. Illustration. Vehicle platoon. 

Cooperative Merge 

Cooperative merging leverages V2V and vehicle-to-infrastructure (V2I) communications to 

enable an ADS vehicle to signal to other vehicles (e.g., via DSRC) its intention to merge into a 
7 

traffic stream. Using signaled information, merging vehicles may identify upcoming acceptable 

gaps on the mainline and make lane changes when possible. In addition, upstream managed lane

vehicles may cooperate by adjusting their speeds or changing lanes to create a gap for the 

requesting vehicle. The trajectories of merging vehicles are then optimized such that the 

disturbance created in the traffic stream is minimized. The merge movement can then occur 

safely and with minimal impact on the platoon and upstream traffic. 

A recent study described developing a vehicle control platform to successfully conduct a 

proof-of-concept field experiment of a cooperative lane change maneuver driven by a simple 

algorithm (Raboy et al. 2017). This demonstration was executed using automated speed control, 

V2V communications, and vehicle-based radar systems. Experimental results showed the 

effectiveness of the platform and successful proof of the new concept of cooperative lane 

change. Chou, Shladover, and Bansal (2016) tested two cooperative automated merging 

strategies for highway entry: one using V2I communication and the other using V2V 

communication in a microscopic simulation. The results showed that V2I reduced travel time in 

the merging section when the traffic flow was high, and the V2V case supports a significant 

increase in traffic flow without increasing travel times. The results indicate the potential 

advantages of using cooperative automation to relieve the bottleneck in the merging section. 

These studies only target limited merging areas with simple algorithms. The effectiveness of 

cooperative merging cannot be isolated from other CDA operations, such as platooning. 

Additionally, the combination of cooperative merging with speed harmonization (by controlling 

and coordinating arrivals of upstream managed lane vehicles to create gaps for merging) can 

further improve merging area performance. To solve the two challenges of separate and simple 

algorithms, IHP1 extended the isolated cooperative merge by allowing merging vehicles to join 

the mainline managed lane platoon from the rear of the platoon. As shown in figure 2, the 

merging vehicle will form a virtual platoon with the two mainline vehicles, with one being the 

last vehicle of the first platoon and the second, the immediate follower of the mainline platoon (a

vehicle or leader of the second platoon). This virtual platoon will be coordinated by the roadside 

infrastructure and will last until the merge vehicle makes a lane change to the mainline.
8 

Source: FHWA.

A. Scenario 1. 

Source: FHWA.

B. Scenario 2. 

Figure 2. Illustrations. Comparison between regular human merge and cooperative merge.

Speed Harmonization

Speed harmonization involves gradually lowering vehicle speeds upstream of a heavily 

congested area to reduce stop-and-go traffic, which contributes to driver frustration and crashes. 

This strategy may also be used to reduce vehicle speeds, to either delay or prevent the onset of 

traffic congestion. To date, a related strategy known as variable speed limits (VSL) has been 

applied at several locations in Europe and a few locations in the United States. Installations in 

Europe, some of which date back to the 1970s, have shown positive results in improving traffic 

flow stability, reducing crashes and injuries, and decreasing emissions using VSL (Fuhs and 

Brinckerhoff 2010). Current VSL systems use changeable speed limit signs posted over each 

lane to regulate freeway speeds based on prevailing traffic conditions. Although VSL systems 

may achieve speed harmonization when successful, they were not considered to be a connected 

vehicle application by IHP1. The reason they are not is because dynamic speed limit adjustments 

are less efficient than dynamic adjustments of recommended and/or actual speed limits 

communicated directly to CDA vehicles. In an ideal scenario, speed commands are generated by 

effective algorithms based on real-time traffic monitoring. Different commands are then 

communicated to vehicles on different segments of the roadway and automatically implemented 

by the vehicles. Such dynamic speed harmonization systems may successfully manage upstream 

and bottleneck (e.g., merging area) traffic flow through the following actions: 
9 

• Reliably detecting the location, type, and intensity of downstream congestion (or other 

relevant) conditions.

• Formulating an appropriate response plan (i.e., vehicle speed and/or lane 

recommendations) for approaching vehicles. 

• Rapidly disseminating such information to upstream vehicles in a manner that achieves 

an effective compliance rate.

Some recent studies assessed V2I-based speed harmonization in which speed commands were

directly communicated to vehicles (Learn et al. 2017; Talebpour, Mahmassani, and Hamdar 

2013; Ghiasi, Li, and Ma 2019; Yang and Rhaka 2017). These simulations found significant 

travel time reductions (e.g., a 10 percent reduction corridor-wide and a 35 percent reduction on 

localized bottleneck segments) at CAV penetration rates of 10 percent or higher. This finding 

concurred with those of other simulation-based studies (Talebpour, Mahmassani, and Hamdar 

2013). See Ma et al. (2016) for a comprehensive review of algorithms for VSL and speed 

harmonization. 

In IHP1, segment-based traffic speed harmonization was considered. This harmonization refers 

to the control strategy, which divides the roadway into segments and generates the same speed 

commands or recommendations across each segment of a roadway. The goal is to ensure all 

vehicles travel at similar speeds, thereby minimizing conflicts among vehicles and improving 

efficiency and safety of the system.

As shown in figure 3, in the case of a downstream speed drop in the bottleneck area, if the 

congestion is moderate, the algorithm will seek to smooth the traffic in the speed harmonization 

zone, let the queue at the bottleneck dissipate, and then allow the upstream traffic to pass the 

bottleneck smoothly at a reasonable speed. Otherwise, if the traffic is too congested and the 

queue is not anticipated to dissipate in a short time, the CDA vehicles will be guided by the 

infrastructure to approach and join the queue smoothly and, in turn, avoid hitting the downstream 

queue at a sudden full stop. 

Source: FHWA.

Figure 3. Illustration. Infrastructure-to-vehicle speed control algorithmic logic. 
10 

IHP1 implemented speed harmonization in response to downstream bottleneck conditions, 

recurring capacity constraints, or non-recurring events (e.g., work zones) by sending 

corresponding general speed commands for certain roadway segments. The longitudinal speed 

commands can be extended to better integrate with cooperative merging at merge areas to 

effectively create gaps for merging vehicles. However, this level of integration results in more 

computational burden due to the attempt to control and coordinate all individual vehicles in real 

time. The combination of a segment-based algorithm at basic segments and a trajectory-based 

algorithm at merging areas can be considered as a feasible compromise.

IHP1 Infrastructure

IHP1 was designed for a managed lane scenario with dedicated ramps to form high-performance 

traffic streams along freeway facilities; therefore, CDA vehicles, eligible to use managed lanes, 

can be fully observed and controlled by the roadside infrastructure to ensure minimum 

disturbance to the mainline traffic when the onramp vehicles merge into the mainline managed 

lane.

Two major factors tie managed lanes to IHP operations. First, although ADS technologies offer 

opportunities to advance safety, mobility, and reliability on the Nation’s roadways, market 

penetration will be low at first, and the potential benefits may not be fully realized. Managed 

lane facilities will support realizing these benefits at early deployment stages by attracting and 

concentrating the equipped vehicles in proximity to each other so they can gain the benefits of 

V2V cooperation. IHP1 focused on deployment stages during low market penetration and 

addressed how the proposed bundling of speed harmonization, cooperative merge, and 

platooning is operated to improve existing system performance. For early deployment, 

single-lane, controlled-access facilities equipped with existing intelligent transportation system 

(ITS) and communication devices (e.g., DSRC RSUs) offer the opportunity to begin integrating 

ADS vehicles into traffic. Because the facilities are single lane, the presence of a small number 

of CDA vehicles can still impact operations on the managed lanes and, therefore, improve 

system performance and the individual traveler’s experience.

Second, IHP1 presented strategies to increase the efficiency of available managed lane capacity 

and to improve traffic performance without significant capital investment (e.g., construction of 

lanes or management of reversible lanes). These strategies are an attractive solution for managed 

lane operators seeking to meet Federal and local performance requirements, or those seeking 

innovative solutions to growing travel demand. More efficient data collection through CDA

technologies may also be mutually advantageous for managed lane facilities to meet performance 

management and reporting requirements. 

Existing managed lanes are better equipped to support IHP1 operations than general purpose 

lanes with existing sensors (e.g., radar detectors) and communication infrastructure (e.g., fiber 

optic). Managed lanes are composed of various systems, subsystems, and components. 

Subsystems commonly found in managed lanes include real-time traffic management and 

communications subsystems, which connect the other subsystems. The communication 

subsystem can be an important asset for CDA operation, as it can support needed V2I systems. 

For instance, a direct fiber-optic connection can be leveraged for transmitting data to the TMC 

while saving the upfront costs to set up a fiber-optic cable on a general-purpose facility. Other 
11 

relevant infrastructure considerations include the roadside communications network, roadside 

electrical power availability, and toll system maintenance. Electronically tolled lanes are 

typically equipped with a variety of sensor technologies that support tolling. These technologies 

can be used and enhanced in a CDA environment to support speed harmonization, vehicle 

platooning, and cooperative merging. 

IHP STAKEHOLDERS

Stakeholders’ actions influence travel in the transportation environment, which may include road 

users traveling across publicly accessible roadways, emergency responders, and infrastructure 

owners and operators (IOOs). This section identifies two types of IHP stakeholders and their 

corresponding needs: road users and IOOs. 

Road Users

A road user is a traffic participant on, or adjacent to, an active roadway for the purpose of 

traveling from one location to another. For IHP, human-driven or connected motorized vehicles

are the main users of the freeway systems. Road user needs include:

• Safe trips.

• Smooth, low-stress, and fast travel.

• Reliable travel times.

• Accurate information to help them make optimal decisions about driving tasks (decision 

support systems). 

From the transportation user’s perspective, IHP supports and enhances the following benefits: 

• Smoother and faster travel with less stress—Vehicle platooning and merge 

coordination can reduce the friction in traffic flow, increase lane capacity, and improve 

vehicle-following stability, thereby increasing the capacity of highway bottleneck 

locations. 

• Greater operational efficiency and travel-time reliability—The combination of speed 

harmonization, cooperative merge, and vehicle platooning can substantially reduce 

uncertainty in travel times by smoothing traffic and enabling real-time prediction of 

travel times.

• More productive travel experience—Several features of CDA technology, including 

speed harmonization, can help improve the travel experience. These features include 

stop-and-go movement elimination, travel time reliability improvement, proactive 

congestion management, and access control. 

Table 2 lists four categories of road users and defines the characteristics and needs for each 

category. 
12 

Table 2. Road user characteristics and needs. 

Driver

Road User 

Categories User Characteristics and Needs

Human driving Regular human 

driver 

Regular human drivers do not have connectivity or 

automation capability. They have uncertain driver 

behavior. Their needs align with general user needs. 

Connected human 

driver 

Connected human drivers receive additional traveler 

information and can make better, informed travel 

decisions. Their needs align with general user needs. 

Automated 

driving 

Nonconnected 

ADS vehicle

Nonconnected ADS vehicles operate independently, 

rely on local sensor information and automated control 

software, and usually have conservative behavior to 

provide increased comfort and safety margin. Their 

needs include accurately sensing local traffic conditions 

and actuating control of vehicles to ensure safety and 

travel efficiency. 

CDA vehicle Compared with ADS, CDA vehicles partner with other 

cooperative vehicles and infrastructure in the traffic 

stream to improve overall traffic performance. Their 

needs include availability of other vehicles to perform 

cooperative actions and improving overall system 

safety and efficiency while guaranteeing individual 

vehicle travel experiences. 

IOOs 

An IOO is a traffic participant who provides, operates, and maintains roadways and supporting 

infrastructure that enable the mobility needs of road users. IOOs include public, public-private, 

or private entities that operate in accordance with Federal, State, and local laws.

The goal of an IOO is safe and efficient traffic management, which includes monitoring and 

managing traffic and the factors affecting traffic flow (e.g., incidents, weather, work zones, 

dissemination of routing information, and other actions that improve traffic flow efficiency). 

Operator goals may also include: 

• Reducing recurring congestion on urban freeways. 

• Improving reliability and safety. 

• Reducing travel times, fuel consumption, and emissions. 

• Maintaining and increasing the use of alternative and emerging transportation modes 

(e.g., car-sharing options). 

Many State departments of transportation (DOTs) and public agencies have developed roadmaps 

for incorporating connected vehicles, ADS, and CDA into future TSMO practices (Virginia DOT 

CAV program, Pennsylvania DOT CAV initiative, California Department of Transportation 

(Caltrans) CAV Infrastructure Development Branch) (VDOT 2021; Pennsylvania DOT 2022; 

Caltrans 2021). Based on State and agency plans and indepth engagement with Virginia DOT
13 

and Transurban, the following benefits of IHP are summarized from the IOO perspective 

(Transurban n.d.): 

• Meeting efficiency goals. Early adoption of CDA on an existing managed lane allows 

greater congestion management ability for facility operators, increased throughput, 

enhanced safety, and improved driver experience. These benefits will be based on the 

relatively higher fraction of CDA vehicles concentrated in the managed lane facility 

compared with the number of vehicles using the entire highway. 

• Improving highway resource utilization. Traditional approaches to managing 

congestion, such as capacity expansion, are increasingly becoming obsolete due to 

funding constraints and limitations of these approaches in alleviating transportation 

problems. CDA technologies can be considered as strategies for TSMO that offer 

potential for innovative solutions to congestion and travel time reliability. 

• Gaining first-mover advantage. If operators currently primed to accommodate CDA

vehicles on their facilities do not voluntarily test and advance this technology, third 

parties may fill that role and dictate the direction of CDA technology development. This 

direction may or may not be aligned with an agency’s goals or organizational capacity. 

• Evolving to accommodate the future of mobility technology. Organizations that 

respond to rapid technological change may be more likely to thrive in this era of 

technological enhancement in the transportation field. 

JUSTIFICATION FOR, AND NATURE OF, CHANGES

The transportation industry is moving toward improving safety with ADS by enhancing various 

vehicle technologies (e.g., levels of automation, ubiquitous sensing using automated vehicle 

sensors). A key question is, what additional capabilities and possibilities can be expected from 

ADS as more advanced sensing and computing capabilities are integrated with ADS? 

Data from these systems become a key resource because an ADS that shares its perception 

information could significantly improve situational awareness of surrounding ADS. If an ADS 

can share tactical or operational plans in the future 5 s, 20 s, or even 1 min, and negotiate with 

other ADS to jointly perform certain maneuvers, transportation disutilities such as crash risks, 

delay, and excessive emissions can be reduced.

From the infrastructure end, upgrading existing infrastructure and software may not be sufficient. 

Advanced infrastructure concepts may instead be necessary, such as cloud services that provide 

digital information (e.g., work zones, incidents, weather, congestion) or management rules (e.g., 

speed or gap control) in a format that can be directly sent to, and processed by, ADS vehicles. 

For freeway control, CDA presents opportunities and new TSMO strategies. Unlike IHP1, the 

IHP2 concepts are enabled by higher SAE levels of automation and various classes of 

cooperation (SAE International 2020). IHP2 aims to fully utilize shared data between vehicles, 

and between vehicles and infrastructure, to create better dynamic world models, referred to as 

cooperative perception, that enable CDA vehicles to navigate traffic streams more safely and 
14 

efficiently. Also, CDA vehicles not only act on additional information, but also actively engage 

with surrounding CDA vehicles under a cooperative control paradigm. For example, CDA 

vehicles can negotiate with each other to obtain optimal trajectories preferred by the vehicles 

themselves and also taking into account the traffic system performance. These capabilities create 

more possibilities for CTM and vehicle coordination to improve the performance of vehicle

behavior, local traffic, and transportation systems. 

Driving automation and connectivity provide opportunities to deploy multiple cooperative 

automation strategies. Successful deployment also depends on coordination among diverse 

stakeholders, including IOOs, ITS technology providers, ADS and ADS-equipped vehicle 

manufacturers and suppliers, and ADS-dedicated vehicle fleet operators. IHP2 provides the 

opportunity to use CDA for freeway system management and congestion reduction. 
15 

CHAPTER 3. INTEGRATED HIGHWAY PROTOTYPE 2 OPERATIONAL CONCEPT

This chapter details the operational concepts of IHP2 by laying out the vision for IHP and 

discussing different IHP2 features. IHP2 is a concept in which the automated driving technology 

can work together with automated vehicles and with roadway infrastructure to improve the 

performance of the transportation system. This chapter describes how automated driving 

technology can be used in a cooperative manner, starting when ADS or CDA vehicles enter the 

freeway until they exit the freeway. This chapter also discusses the role of infrastructure in 

supporting and enabling automated driving technology to help manage the transportation system 

to address congestion and improve safety during normal travel. 

CDA

This section provides an overview of the concept and framework of CDA. The key discussion 

points include the different levels of automation and various classes of cooperation. 

SAE International J3216 

CDA technologies enable mobility applications that are not achievable by individual ADS 

operating independently of each other. These ADS operate by sharing information that can be 

used to increase safety, efficiency, and reliability of the transportation system and accelerate 

deployment of driving automation. Driving automation and connectivity present opportunities to 

deploy multiple cooperative automation strategies, but successful deployment depends on 

coordination among diverse stakeholders. These public and private stakeholders are preparing 

for, and deploying, different use cases at different temporal and spatial scales. For example, 

vehicle strategies—such as speed harmonization—and TSMO strategies—such as basic travel, 

traffic incident management, weather management, and work zone management data sharing—

are key use cases supported by CDA. 

CDA aims to improve the safety and flow of traffic and facilitate road operations by supporting 

the movement of multiple vehicles in proximity to one another. This aim is accomplished, for 

example, by sharing information that can be used to influence dynamic driving task (DDT) 

performance directly or indirectly by one or more nearby road users. Vehicles and infrastructure 

elements engaged in cooperative automation may share information, such as state (e.g., vehicle 

position, signal phase) and intent (e.g., planned vehicle trajectory, signal timing), or seek 

agreement on a plan (e.g., coordinated merge). Cooperation among multiple participants and 

perspectives in traffic can improve safety, mobility, situational awareness, and operations. 

Similar to SAE J3016 levels of automation, a new standard, SAE J3216, defines classes of 

cooperation (SAE International 2020, 2021). The classes address different capabilities of a CDA 

vehicle that would affect its ability to cooperate with other CDA vehicles. These classes are

summarized in table 3. 
16 

Table 3. Overview of SAE International cooperation classes and automation levels (SAE International 2020). 

No Automation

Partial Automation of DDT Complete Automation of DDT

Level 0:

No Driving 

Automation 

(human does 

all driving)

Level 1:

Driver Assistance 

(longitudinal or 

lateral vehicle 

motion control) 

Level 2:

Partial Driving 

Automation 

(longitudinal and 

lateral vehicle 

motion control) 

Level 3:

Conditional 

Driving 

Automation 

Level 4:

High Driving 

Automation 

Level 5:

Full Driving 

Automation 

No Cooperative Automation e.g., signage, 

TCD

Relies on driver to complete DDT and 

supervise feature performance in real 

time

Relies on ADS to complete DDT under 

defined conditions (fallback condition 

performance varies between levels)

SAE class A:

Status 

Sharing

Here I am, 

and what I 

see

e.g., brake 

lights, traffic 

signal 

Potential for improved object and event 

detection*

Potential for improved object and event 

detection**

SAE class B:

Intent Sharing

This is what 

I plan to do

e.g., turn 

signal, merge 

Potential for improved object and event 

prediction*

Potential for improved object and event 

prediction**

SAE class C:

Agreement 

Seeking

Let’s do this 

together

e.g., hand 

signals, 

merge

N/A

C–ADS designed to attain mutual goals 

through coordinated actions 

SAE class D:

Prescriptive 

I will do as 

directed

e.g., hand 

signals, lane 

assignment 

by officials 

N/A

C–ADS designed to accept and adhere to a 

command

© 2020 SAE International.

*Improved object and event detection prediction through CDA class A and B status and intent sharing may not always be realized, given that Level 1 and 2 

driving automation features may be overridden by the driver at any time, and otherwise have limited sensing capabilities compared with Level 3, 4, and 5 

ADS-operated vehicles.

**Class A and B communications are one of many inputs to an ADS object and event detection and prediction capability, which may not be improved by the 

CDA message. 

C–ADS = cooperative automated driving system; N/A = not applicable; TCD = traffic control device. 
17 

CARMA3

The purpose of CARMA is to transform transportation and improve efficiency and safety 

through automated vehicles working together and working with roadway infrastructure. 

CARMA Platform is open-source software that enables researchers and engineers to develop and 

test CDA features on properly equipped vehicles to set the foundation for interoperability across 

vehicle make and model and safely introduce the technology on the Nation’s roads. The most 

recent version enables R&D capabilities to support TSMO. CARMA Platform enables 

cooperative research functionality to an ADS, and CARMA Cloud enables the roadway to 

provide information to support safe operation for new TSMO strategies. CARMA is built on a 

flexible framework designed to be easily shared and integrated into several vehicle types, 

including passenger cars and heavy trucks. 

CARMA technology facilitates cooperation among vehicles and roadway infrastructure through 

communication. By providing information about what is ahead with CARMA Cloud (such as 

basic travel, traffic incident management, road weather management, and work zone 

management), CARMA Platform enables automated vehicles to interact and cooperate with 

infrastructure and other vehicles, thereby increasing the performance of the existing 

transportation system. 

This ConOps serves as part of the CARMA3 framework (Nallamothu et al. 2020) and 

distinguishes between levels of vehicle automation and classes of vehicle cooperation. Four 

classes of cooperation are defined as follows (SAE International 2020): 

• Status-sharing (class A): Perception information about the traffic environment and the 

state of the sending entity are provided by the sending entity for potential use by 

receiving entities.

• Intent-sharing (class B): Information about planned future actions of the sending entity 

are provided by the sending entity for potential use by receiving entities. 

• Agreement-seeking (among CDA vehicles) (class C): A sequence of collaborative 

messages among specific CDA devices is intended to influence local planning of specific 

DDT-related actions.

• Prescriptive (class D): A party has determined a course of action is necessary or optimal 

for its own operations and communicates with other road users with the intention of 

directing their actions.

This ConOps addresses the feature groups that are part of the CARMA IHP2, including CTM, 

CLF, and CLC. These groups correspond to the following use cases and situation groups 

identified in the CARMA3 TSMO ConOps that was developed in a parallel project. 

Use Case: Basic Travel

The TSMO situation groups for the basic travel use case are given as follows:
18 

• Situation Group A-1: Basic Travel Operations for All Roadway Types—

Nonspecialized vehicle situations applicable to all roadway types: 

o A-1.1: Managing vehicle platooning (joining, formation, operation, and dissolution). 

o A-1.2: Managing vehicle strings CACC. 

o A-1.3: Implementing cooperative lane changes (when not platooning). 

o A-1.5: Identifying vehicle by capabilities (e.g., automation level and cooperation 

class).

o A-1.10: Navigating managed lanes facilities (high-occupancy toll lanes).

• Situation Group A-2: Basic Travel Operations for Highways—Nonspecialized 

vehicle situations applicable to roadways with intersections that are not at grade to other 

roadways, which include interstates and most freeways and expressways: 

o A-2.1: Merging on/diverging off highway (when not platooning). 

o A-2.2: Merging on/diverging off highway with platoon. 

o A-2.3: Managing active traffic (e.g., speed harmonization). 

o A-2.4: Ramp metering with/without traffic signal prompt.

TECHNOLOGICAL FRAMEWORK FOR IHP2 

This section describes the functionality needs for IHP2. The focus is to discuss IHP2 enabled by 

higher levels of automation and classes of cooperation. Other lower levels of automation and 

cooperation scenarios will also be discussed. 

In IHP1, three features were developed separately and then integrated: platooning, cooperative 

merge, and speed harmonization. IHP1 relies on SAE Level 1 automation and integration done 

only for longitudinal (speed/acceleration) control (SAE International 2020). Higher levels of 

automation offer a larger number of possible CDA scenarios, and, therefore, IHP2 adopts a 

different feature definition that better aligns with regular highly automated vehicle functional 

modules. 

Figure 4 shows a list of the planning module stack of CARMA3. The boxes with a dark 

background on the left are for regular noncooperative ADS modules/features. They are used 

when one ADS vehicle cannot find other vehicles or infrastructure with which to engage for

cooperative maneuvers. Consideration of noncooperative ADS modules is key when the market 

penetration of CDA vehicles is low during initial deployment stages. The boxes with a lighter 

background are cooperative feature groups to enable various cooperative maneuvers. Other than 

conventional module types at the vehicle level, such as lane follow and lane change, two lighter 

yellow boxes are added for system-level management: CTM and cooperative accessible 

transportation. 
19 

Source: FHWA.

Figure 4. Illustration. CARMA planning modules. 

For IHP2, the three relevant feature groups are CLF (platooning and CACC), CLC (cooperative 

lane change, merge, and weave), and CTM (speed and gap control, lane assignment, and queue 

management). Table 4 defines each feature within the three feature groups. Each of the three 

IHP2 feature groups not only includes IHP1 features, but also covers additional features (e.g., 

cooperative lane change, gap control) to present a comprehensive package for integrated freeway 

management with CDA.
20 

Table 4. IHP2 features and descriptions.

Feature 

Group IHP2 Feature Description

CLF CACC (strings) Allows two or more vehicles to communicate with one 

another and, therefore, follow in a string, closer than would 

be possible without communication. However, each vehicle 

still behaves independently of other vehicles and has no 

responsibility toward the rest of the string.

Platooning 

(groups) 

Allows two or more vehicles to closely travel together as a 

single unit, where each vehicle agrees to abide by the group 

rules to proceed safely through traffic and realize maximum 

benefits.

CLC Cooperative lane 

change 

Plans a smooth lateral motion from the current lane into an 

adjacent lane by first checking for collision risk with 

neighboring vehicles in the target lane and initiating 

cooperative agreements with one or more of them, as 

necessary, to plan a safe lane change within the physical 

constraints of the situation.

Cooperative merge Plans a smooth lateral motion from the current lane, which 

is either ending or combining with another (e.g., when two 

highways merge), to the lane into which the current lane 

merges. Uses cooperative agreements with neighboring 

vehicles, as necessary, to ensure safety and efficiency.

Cooperative 

weave

Allows two or more cooperative driving automation 

vehicles to plan simultaneous or near-simultaneous lane 

changes where each vehicle will be changing lanes into the 

lane of the other vehicle.

CTM Speed control Allows a vehicle to adjust its speed based on 

communication/rules from other vehicles, the cloud, or 

another entity (e.g., pedestrian).

Gap control Allows a vehicle to adjust its gap to its preceding vehicle, 

whether part of a string, group, or neither, based on the 

communication/rules provided from other vehicles or the 

cloud.

Lane assignment Accepts a request from the cloud regarding which lane the 

vehicle should plan to be in, and, if necessary, when 

appropriate, calls for the lane change and/or merge features 

to be executed.

Queue 

management

Allows vehicles to accept speed control and lane 

assignment command such that the downstream bottleneck 

queue can be minimized.
21 

CLF

The CLF feature group contains two distinct features: CACC and platooning. CACC allows two 

or more vehicles to communicate with one another and follow in a string closer than would be 

safely possible without communication. However, each vehicle still behaves independently of 

other vehicles and has no responsibility toward the rest of the string. Platooning allows two or 

more vehicles to closely travel together as a single unit, where each vehicle agrees to abide by 

the group rules to safely proceed through traffic and realize maximum benefits. Table 5

summarizes the differences between CACC and platooning. 

Table 5. Differences between platooning and CACC.

Category Platooning CACC

Control hierarchy Hierarchical control with special 

responsibilities for platoon 

leader.

Decentralized control with no special 

responsibilities for the string leader.

Membership Coordinated platoon/group 

membership.

Ad hoc string membership and 

vehicles behave independently.

Spatial scope Operates in a single lane or in 

multiple lanes for a platoon lane 

change, search for partners, and 

so on.

Operations in a single lane with 

small following gaps. 

Platooning is an organized behavior where each vehicle in a platoon has a responsibility toward 

the rest of the platoon to abide by agreed-upon rules. The focus is on organized behavior. With 

platooning, a group of vehicles, coordinated by the platoon leader, aim to move through traffic as 

safely and efficiently as possible. With CACC, independent vehicles can receive the front 

vehicles’ real-time information and, thus, can follow more closely compared with conventional 

ACC. 

With platooning, the vehicles behave freely before joining and after leaving the platoon. Once 

they have agreed to join the platoon, and then subsequently join, the vehicles must follow certain 

rules and protocols. The rules are commonly set by the platoon leader. A platoon leader may set 

rules by passing on the rules it receives from infrastructure (e.g., maximum amount of vehicles 

allowed in a platoon or speed limit) or by its own volition (e.g., joining allowed only from the 

side or just from the rear, and, if joining is allowed from the side, how additional space will be 

created). 

Because platooning is a group activity, it does not need to be confined to a single lane. A platoon 

may change lanes together, and vehicles would coordinate how this is done. For example, they 

might coordinate the last vehicle changing lanes first and then each subsequent vehicle changing 

lanes in reverse order, from last to first. Platooning protocols can also help with looking for 

platooning partners and agreeing where to meet, as these are part of the team activity.

In this ConOps, platooning and CACC coexist as CLF features because of the different classes of 

cooperation. CACC vehicles belong to class A (status sharing), while vehicle platooning can 

vary with class B (intent sharing), class C (agreement seeking), or class D (prescriptive). 
22 

Figure 5 illustrates the CLF. A five-vehicle platoon is operating in the left-most lane, and the 

platoon leader coordinates the intraplatoon gaps and longitudinal maneuvers. In the middle lane, 

one CDA vehicle has completed negotiating with the platoon leader and is allowed to change the 

lane to join the platoon from the rear of the platoon. This maneuver may be allowed because this 

CDA vehicle might share the same (or nearby) destination off-ramp with other platoon members, 

and vehicles joining together in a platoon is operationally preferred. This concept will be further 

discussed in the CTM section. In the middle lane and the right-most lane are a four- and 

three-vehicle CACC string, respectively. The intrastring gaps between vehicles are different 

from each other, and this value is set by the independent CDA vehicles themselves. In the 

platoon, the intraplatoon gap can be set as the same or different among platoon members, but the 

platoon leader must approve it. 

Source: FHWA.

Figure 5. Illustration. IHP2 CLF. 

CLC

Unlike IHP1, which only uses Level 1 automation, IHP2 focuses on benefits from using 

functionalities of Level 3, or higher, automation. A major distinction is the potential for both 

longitudinal and lateral vehicle coordination. Three features are included in the CLC feature 

group: cooperative lane change, cooperative merge, and cooperative weave.

Cooperative Lane Change 

Cooperative lane change plans a smooth lateral motion from the current lane into an adjacent 

lane. It first checks for collision risk with neighboring vehicles in the target lane and initiates

cooperative agreements with one or more of them to plan a safe lane change within the physical 

constraints of the situation. 

This process occurs any time a lane change needs to be initiated. Ideally, CDA vehicles can 

receive the current status and intent of surrounding vehicles and negotiate with them, depending 

on the surrounding vehicles’ cooperation classes, to complete the maneuvers. When

nonconnected, human-driven vehicles are nearby, CDA vehicles rely on onboard sensors to 

detect, estimate, and predict human-driven vehicle behavior. 

Figure 6 illustrates one example where the ego CDA vehicle A in the right lane intends to change 

the lane and join a platoon in the left lane from the rear. Vehicle A constantly checks collision 
23 

risks with the vehicles in the front in the current lane (vehicle D), the last vehicle in the platoon 

(vehicle C), and a nearby CDA vehicle in the left lane (vehicle B). If vehicle B can negotiate 

with vehicle A, vehicle B may control its longitudinal maneuvers to leave a gap that is 

sufficiently safe for vehicle A to change the lane and join the front platoon. If vehicle B only 

shares the current status or intent, it is still beneficial for vehicle A to plan the trajectory better to 

smoothly change the lane to the left.

Source: FHWA.

A. Step 1. 

Source: FHWA.

B. Step 2. 

Figure 6. Illustrations. Cooperative lane change.

Cooperative Merge 

Cooperative merge plans a smooth lateral motion from the current lane, which is either ending or 

combining with another (e.g., when two highways merge), to the lane into which the current lane 

merges. Cooperative merge seeks cooperative agreement with neighboring vehicles to ensure 

safety and efficiency.

When a vehicle intends to merge into the mainline, it can potentially encounter four cases that 

will activate the cooperative merge. Figure 7 shows a typical example of different scenarios 

during a merge process and the corresponding cooperative driving logic. A cooperative merge

can be applied in a V2V or V2I environment or both. An approach that includes V2I is beneficial

because it can facilitate, through the use of roadside infrastructure or a cloud service, the 

cooperative merge process by allowing the cooperation process to start earlier, resulting in a 

higher level of benefit. The following four cases can activate cooperative merge:
24 

Source: FHWA.

A. Case 1.

Source: FHWA.

B. Case 2.

Source: FHWA.

C. Case 3.

Source: FHWA.

D. Case 4.

Figure 7. Illustrations. Cooperative merge.
25 

• Case 1: In figure 7-A, vehicle B is in the target lane in front of the merging vehicle A. If 

vehicles A and B have similar speed (i.e., small speed difference), the merging vehicle A 

slows down slightly and merges into the mainline. 

• Case 2: As shown in figure 7-B, if vehicle A intends to merge into the mainline and 

vehicle B in the target lane is behind vehicle A, then vehicle B will be advised to move 

cooperatively over to the adjacent lane to create a safe and acceptable gap for vehicle A 

to merge into. This cooperative merge can be activated under certain conditions, such as 

when the new lane will not affect vehicle B’s ability to complete its original route and 

when the speed difference between vehicle A and vehicle B is less than maximum speed 

difference threshold. 

Case 3 and case 4 are scenarios in which the mainline vehicle cannot move over into the adjacent 

lane because the requirements in case 2 cannot be met. Therefore, the mainline vehicle needs to 

reduce speed to create a gap and allow the merging vehicle to enter the mainline.

• Case 3: As shown in figure 7-C, vehicle A on the onramp intends to merge into the 

mainline, and mainline vehicle B is behind vehicle A. In this case, vehicle B will slow 

down to create an acceptable gap to let vehicle A merge into the mainline from the 

onramp. Meanwhile, the following vehicle C will also cooperatively slow down to keep a 

safe following distance from vehicle B. Vehicles B and C can be independent vehicles 

and can also possibly be part of a platoon. 

• Case 4: Figure 7-D shows that when vehicle A requests to merge into traffic from the 

onramp, mainline vehicle B is too close to slow down. In this case, vehicle B will take no 

action and keep its speed. The following vehicle C will slow down and let vehicle A 

merge into the mainline.

Although the existence of human-driven vehicles is not discussed in the four cases, any ADS 

vehicle can complete the merge maneuver independently through noncooperative modules, 

relying on onboard sensing and ADS software. However, it is always possible for any CDA 

vehicle to sense, estimate, and predict human-driven vehicle trajectories to enable cooperative 

driving with human-driven vehicles. 

Cooperative Weave

Cooperative weave allows two or more CDA vehicles to plan simultaneous or near-simultaneous 

lane changes, where each vehicle changes lanes into the lane of the other vehicle. Involved CDA

vehicles will either receive information from, or negotiate with, surrounding vehicles and plan 

their own trajectories to complete the weave maneuver smoothly. For example, as shown in 

figure 8, vehicle A intends to make a lane change to the left to join the platoon (figure 8-A), 

while vehicle B intends to change lanes to the off-ramp (figure 8-B). If no cooperation is 

enabled, one or both vehicles may need to accelerate or brake heavily to ensure safety as they 

start to get close to each other. With CDA, it is possible the two vehicles can negotiate and 

codevelop optimal smooth trajectories that eliminate the need for large accelerations and 

minimize the impact on the upstream traffic. In this example, vehicle A slightly accelerates and 
26 

changes the lane by knowing vehicle B agrees to slow down slightly and make the lane change 

after vehicle B completes the maneuver. 

Source: FHWA.

A. Step 1. 

Source: FHWA.

B. Step 2. 

Figure 8. Illustrations. Cooperative weave. 

CTM

Both CLF and CLC are mainly V2V cooperation features. CTM includes three features: speed 

control, gap control, and lane assignment. CTM focuses on cooperation between vehicles and 

infrastructure. Since TMCs collect data from all connected vehicles and infrastructure detectors

and have better estimates of dynamic traffic states, the TMC can make better informed decisions 

to improve traffic system performance.

Speed control allows a vehicle to adjust its speed based on communication/rules from other 

vehicles, the cloud, or another entity. Speed control is similar to the speed harmonization concept 

in IHP1 for bottleneck reduction (e.g., capacity constraints, work zones). Additionally, speed 

control for IHP2 also concerns speed control of CDA vehicles to improve local traffic 

smoothness and stability. For example, at key freeway locations, such as merge and weave (as 

shown in figure 7 and figure 8), the longitudinal speed of vehicles A and B can be controlled by 

the infrastructure to achieve system-level performance, not just the vehicle-level performance for 

vehicles A and B. 

Speed control of each CDA vehicle results in coordinated space-time trajectories of each vehicle 

to achieve optimal system performance. Recent simulation studies (Ghiasi, Li, and Ma 2019) and 
27 

field experiments (Ma et al. 2016) prove the potential of such an approach in enhancing traffic 

smoothness and, thus, improving efficiency and safety. For example, as shown in figure 9, when 

detecting an imminent downstream speed drop at a bottleneck, to avoid hitting the downstream 

queue at a sudden full stop, a CDA vehicle (the thicker green curve) should moderately slow 

down and pass the bottleneck smoothly at a reasonable speed just as the downstream queue 

dissipates. This trajectory-control strategy not only smooths the CDA vehicle’s trajectory, but 

also helps any type of following vehicles (CDA or human-driven vehicles) to move in a similarly

smooth manner. As a result, vehicles following this CDA vehicle will pass the bottleneck with a 

larger throughput rate due to reduced time headway at a high speed, less fuel consumption due to 

smoothed trajectories, and less collision risk due to harmonized vehicle speed. 

Source: FHWA. 

A. Benchmark without trajectory smoothing. 

Source: FHWA.

B. Harmonized trajectory.

Figure 9. Charts. Projected trajectories with and without smoothing.

Gap control allows a vehicle to adjust its gap to its preceding vehicle, whether it is part of a 

string, group, or neither, based on the communication and rules provided by other vehicles or 

from the cloud. Gap control is another key longitudinal control parameter to enhance system 

efficiency. For example, at merge areas, the infrastructure can instruct CDA vehicles to follow 

closely (i.e., create smaller gaps), in combination with speed control, to create additional large 

gaps for merge vehicles. Gap control may also be instructed by the infrastructure based on 

current weather conditions or unique roadway geometry, which may influence wireless 

communications’ reliability, to ensure safe CDA operations.

In terms of lateral control, lane assignment accepts a request from the infrastructure regarding 

which lane the vehicle should plan to be in. Also, if necessary and when appropriate, it accepts 

calls for the lane change or merge features to be executed. For example, similar to the single-lane 

operations in figure 9, speed control and lane assignment commands are for minimizing

downstream bottleneck queues. Also, when a downstream lane is closed due to a work zone, it is 

possible to assign CDA vehicles to the lanes that are not closed before the vehicles approach the 

work zone. This step is similar to the effect of early lane change advisories, but with CDA 

vehicles, it is possible to perform a system-level optimal control with the flexibility of CDA lane 

assignment. 
28 

INFRASTRUCTURE CONFIGURATION AND NEEDS

This section describes technological and institutional infrastructure and explains the use of 

CARMA Cloud and the role of IOOs in developing the strategies for addressing congestion. 

A key feature of CDA operations is the dynamic vehicle-infrastructure interactions, particularly 

the exchange of real-time vehicular and roadway information that ADS can understand and 

share. IHP2 considers a cloud-based service that can be used to emulate a TMC for traffic 

monitoring, environmental monitoring, operations control and event injection, and road segment 

control definitions (e.g., dynamic speed limits). Such cloud services can communicate with CDA

vehicles through roadside points of presence, irrespective of the particular communications 

technology, using the appropriate protocols. CDA vehicles can also share their statuses and what 

they sense about the surrounding dynamic traffic environment for better static and dynamic 

world models. The two-way information exchange constitutes the foundation of CDA, which 

includes both cooperative perception and cooperative vehicle control/traffic management. CDA 

device agents, vehicles, and infrastructure may use this information to improve situational 

awareness and expand their operational design domain. 

There is a limited set of user needs relevant to the TMC-vehicle/operator-traveler interactions. 

Travelers are primarily the beneficiaries of these interactions but can also be information 

providers. Traffic operators, working on behalf of the infrastructure, are primarily the service and 

information providers but can also be informed by travelers using V2I communication. Table 6

lists the needs of road users and IOOs. In this table, road users are vehicles with connectivity 

capability, such that one-way or two-way information exchanges can occur between road users 

and IOOs. From the communication perspective, traffic management services, or TMC cloud 

services, can rely on DSRC or other communication channels (e.g., cellular services) that are 

subject to small communication delays and few packet drops. As long as the information is

communicated between vehicles and the TMC cloud within a reasonable amount of time (on the 

order of seconds), the information maintains its value for guiding vehicles and informing TMCs. 
29 

Table 6. Infrastructure needs for road users and IOOs. 

Road Users IOOs 

• Get maps for navigating to their 

destination, including detours. 

• Get information on traffic conditions 

ahead. 

• Get information on incidents. 

• Get information on lane use restrictions. 

• Get information on work zones. 

• Get information on weather conditions. 

• Get information on current local speed 

limits. 

• Get information on any special rules 

currently enforced. 

• Get information on toll pricing. 

• Get information on parking availability. 

• Inform IOOs of observed incidents. 

• Inform IOOs of observed work zones. 

• Inform IOOs of observed weather 

conditions. 

• Inform IOOs of their status, intent, and 

what they see. 

• Monitor traffic conditions, including the 

presence of incidents. 

• Monitor environmental conditions. 

• Characterize incidents (location; vehicles, 

people, and objects involved; lanes 

blocked). 

• Inform emergency services of incidents. 

• Change traffic signal timing plans. 

• Control access to roadways. 

• Control speed limits. 

• Control (restrict) lane use. 

• Inform travelers of incidents. 

• Inform travelers of lane use restrictions. 

• Inform travelers of work zones. 

• Inform travelers of weather conditions. 

• Inform travelers of current local speed 

limits. 

• Inform travelers of any special rules 

currently enforced. 

• Receive traffic condition information 

from travelers. 

The vehicle-to-everything communications environment is dynamic and not assumed to be 

perfectly reliable. Therefore, it cannot be expected that every transmitted message is received. In 

many cases this situation is acceptable, but guaranteed delivery may be desirable during complex 

negotiations among vehicles and with the infrastructure. One example of this need for guaranteed 

delivery is the CARMA3 mobility message specification and the protocol with which it is 

normally used, which allow negotiations based on requests and responses in the form of ACK 

(acknowledge) or NACK (not acknowledged) replies. For example, a vehicle broadcasting a 

request to merge waits until an ACK reply is received to confirm the merge will be accepted. If 

the requesting vehicle receives a NACK reply, or no reply at all, it adjusts its plans accordingly 

and attempts a different proposed merge (e.g., at a later time), or does not attempt to merge at all. 

In this way, if the request message is corrupted due to an error introduced by the 

communications medium, it would not result in an unsafe merge because the recipient would not 

send the necessary ACK reply. In other cases where messages are repeated regularly, such as 

speed or gap recommendations, guaranteed delivery is not as important because there will be 

many other opportunities to receive missed messages. 

CARMA Cloud acts as a virtual TMC that receives information about the transportation system 

behavior, determines appropriate traffic behavior, and provides traffic direction and traveler 

information messages to vehicles to maintain safety and mobility across the system. To build 

those messages, CARMA Cloud gathers the needed data from external data sources (e.g., an 

agency’s own sensor networks or third-party and emerging data providers) that provide traffic, 
30 

incident, work zone, and road weather data, as well as any other relevant data. In many cases, 

these data may be provided by ITS operated by a transportation agency in a TMC. As such, 

CARMA Cloud generally supplements existing TMC capabilities with the specific intent to

provide messaging for V2I applications. Because not all roadways are managed through a TMC, 

the platform is data agnostic (i.e., does not use a specific type of data) and can manage the data 

and interfaces needed to provide V2I messaging independent of a TMC. 

To enable IHP2, CARMA Cloud or other selected cloud services can also provide infrastructure 

rules to CDA vehicles, as shown in table 7. 

Table 7. Exchanges and rules between CDA vehicles and the cloud. 

Exchange Type Information Type 

Mapping rules • Updates to lane configuration. 

• Updates to dynamic world models. 

Planning rules • Speed rules. 

• Speed harmonization. 

• Minimum gap rules. 

• Platooning statues (allowed or not). 

• Platooning limitations (two-, three-, or four-car, and so on). 

Cooperative perception • Vehicle current status, intent, and so on. 

• Local world information sensed by each CDA vehicle. 

SUMMARY OF IHP2 NEEDS

Table 8 lists operational needs for key features of IHP2 and provides information for future 

development of the IHP2 system. 

Table 8. Vehicle-side operational needs for IHP2. 

Key Feature ID No. Operational Need

General IHP-N01 Improve safety and efficiency of freeway traffic at the 

basic segment, merge, and weave areas.

IHP-N02 Maintain situational awareness, i.e., accurate dynamic 

world models, including infrastructure (e.g., roadway 

geometry, roadway diet) and surrounding traffic (e.g., 

vehicles, pedestrian, bicyclists). 

IHP-N03 Detect vehicles incapable of cooperating with other 

vehicles.

IHP-N04 Gather, integrate, process, and disseminate data.
31 

Key Feature ID No. Operational Need

CDA Vehicle System:

CLF

IHP-N05 Location, speed, and other status information from 

other CDA vehicles in the vicinity, on the same lane or 

adjacent lanes, of the ego CDA vehicles.

IHP-N06 Location, speed, and other status information from 

human-driven vehicles in the vicinity, on the same lane 

or adjacent lanes, of the ego CDA vehicles. 

IHP-N07 Reliable, low-latency wireless communication to 

enable close lane following for platooning and CACC.

IHP-N08 Negotiate with platoon members or nearby vehicles 

with the intention to join, if the ego vehicle is a platoon 

leader. 

CDA Vehicle System: 

CLC

IHP-N09 Merge/lane-change vehicle speeds, locations, and other 

status information from nearby vehicles. 

IHP-N10 Negotiate with merge/lane-change vehicle and create 

gaps, along with other mainline/target lane vehicles, to 

facilitate merge.

IHP-N11 Mainline/target lane vehicle speeds, locations, and 

other status information from nearby vehicles. 

IHP-N12 Negotiate with the mainline/target lane vehicles for gap 

creation to facilitate merge/lane change.

IHP-N13 Reliable, low-latency wireless communication during 

the gap creation and lane change.

CDA Vehicle System: 

CTM

IHP-N14 Capability for CDA vehicles sharing information of the 

ego vehicles and surrounding CDA and non-CDA 

vehicles with the traffic management service.

IHP-N15 Capability for CDA vehicles to process various 

information sent from the traffic management service.

IHP-N16 Execute CTM commands or respond with appropriate 

control based on information sent from the traffic 

management service.

Non-ADS (Connected 

Human Driver) 

IHP-N17 Information from surrounding vehicles. 

IHP-N18 Share real-time status information with nearby vehicles 

and the traffic management service.

IHP-N19 User interface to present information to human drivers. 

IHP-N20 User interface to show the intent of vehicles in the 

vicinity to merge into the highway or change lanes into 

the ego vehicle lane.
32 

Key Feature ID No. Operational Need

Traffic Management 

Service (Cloud 

Service)

IHP-N21 Gather and process data from infrastructure ITS

devices to monitor current traffic conditions. 

IHP-N22 Gather and process data from CDA-equipped vehicles 

and connected non-CDA vehicles to monitor current 

traffic conditions.

IHP-N23 Gather dynamic information on various traffic events 

(e.g., incidents, work zones, weather, lane restrictions, 

dynamic speed limits) from conventional infrastructure 

owner and operator data sources.

IHP-N24 Gather dynamic information on various traffic events 

(e.g., incidents, work zones, weather, lane restrictions, 

dynamic speed limits) from CDA vehicle and 

connected non-CDA vehicle observation. 

IHP-N25 Set mapping rules and update dynamic world models 

for CDA vehicles.

IHP-N26 Set planning rules, including speed rules, gap rules, 

platooning rules (e.g., size limitations).

ID = identification.

PERFORMANCE METRICS

The effectiveness of IHP2 needs to be evaluated for its capability to positively impact vehicle 

behavior and traffic flow performance metrics. 

Performance Metrics for Vehicle Behavior

Key performance metrics for monitoring and evaluating operations include: 

• Separation distances—Longitudinal distances between the vehicles in the test; used to 

determine safe distances and the frequency of infringement of those distances. 

• Disengagements—Occurrence frequency when safety drivers deactivate the ADS feature 

being tested and take manual control of the vehicles. 

• Travel speeds driven—Speeds driven by each vehicle during the tests used to create an 

accurate picture (playback) for evaluating the driving within the vehicle travel areas. 

• Speed changes—Changes in speeds of the vehicles above or below a threshold in 

response to interactions between ADS vehicles (reduction in speeds by the ramp vehicle 

moving onto the highway and reductions/maintenance of the mainline vehicles’ speeds). 

• Data exchanges during negotiation (cooperation class C)—All data exchanges between 

two cooperation class C ADS vehicles to determine whether the maneuver negotiations 

took place as designed. Exchanges include the following data types: 
33 

o Total duration of the negotiation process. 

o Frequency of negotiation success/failure (NACK replies from neighboring vehicles). 

o Number of attempts before a plan is accepted by all affected neighbors. 

o Message latency, which is the time difference between message origination from

vehicle A to message reading by vehicle B. The latency time includes the following: 

the time to compose the message; the time to send the message from vehicle A’s 

guidance computer to vehicle A’s onboard unit (OBU); the queuing time on vehicle 

A’s OBU; the radio transmission from vehicle A to vehicle B; the message 

constitution and queuing on vehicle B’s OBU; sending the message from vehicle B’s 

OBU to vehicle B’s guidance computer; and the time for vehicle B’s decomposition 

and reading. 

Performance Metrics for Traffic Performance

This subsection identifies performance metrics for traffic performance to evaluate the IHP2 

impacts on traffic flow. As shown in table 9, the five categories of impacts are safety, 

throughput, flow stability, flow breakdown and reliability, and sustainability (Mahmassani 

2016). The impacts can be investigated using analysis, modeling, and simulation (AMS) tools 

and real-world studies. 

Table 9. Traffic performance measures for IHP2 evaluation. 

Category Impact Performance Measure

Safety Reduction in number of crashes Number of crashes 

Improvement in outcome of 

crashes

Severity of crashes

Throughput Increase in traffic flow volume Number of vehicles per hour per lane 

Smoothness of traffic flow Variability of speeds within traffic 

stream

Flow stability Improved local stability Local flow stability index

Improved string stability Mixed-flow string stability index 

Flow breakdown 

and reliability

Occurrence of traffic shock 

waves 

Number of significant shock waves 

formed

Severity of shock waves Propagation speed of formed shock 

waves relative to wave front

Duration of shock wave-induced 

queues 

Sustainability Impact on greenhouse gas 

emissions

Level of carbon dioxide, nitrogen 

oxide, and particulate 

matter-equivalent emissions

Reduction in energy consumption Amount of energy consumed 

Safety

Because the majority of crashes are due to human error, automated vehicles have the potential to 

significantly decrease the number of crashes, specifically at high market penetration levels. One 
34 

way to quantify safety improvements is by calculating the number of crashes and the crash

severity. Other factors, such as safety surrogate measures (e.g., time to collision), are also useful.

Throughput

CDA technologies are expected to increase the flow throughput of transportation facilities by 

increasing flow densities. However, such impacts are dependent on the market penetration of 

those technologies. Throughput can be quantified by measuring the number of vehicles passing 

through per hour and the variability of speeds within a facility segment. 

Flow Stability

There are two types of traffic stabilities: flow stability and string stability. Flow stability refers to 

the traffic stream’s ability to recover its steady-state properties (density-speed) after incurring a 

perturbation. String stability refers to intervehicular spacing along the platoon. If disturbances in 

vehicle spacings do not grow as the disturbance propagates along the platoon, the platoon is 

string stable. There are several stability indexes developed in the literature that can be used in the 

AMS tool (Darbha and Rajagopal 1999; Zhou and Peng 2005). 

Flow Breakdown and Reliability 

Flow breakdown is a traffic phenomenon in which throughput/capacity drops due to a 

perturbation (e.g., accident or sudden braking). CDA vehicles are expected to improve traffic 

flow reliability by providing smoother, safer, and more responsive vehicle operations. The AMS 

tool can use multiple measures to quantify CDA impact on flow breakdown and reliability, such 

as the occurrence of shock waves and the severity of shock waves formed.

Sustainability 

The environmental impact of CDA is uncertain. However, smoother operations associated with 

CDA can lead to lower greenhouse gas emissions and energy consumption due to reduced 

acceleration and braking. 
35 

CHAPTER 4. OPERATIONAL SCENARIOS

This chapter identifies IHP2 operational scenarios to enhance TSMO. The focus IHP enabled on 

any highway in general purpose lanes and understanding the impact as early deployment benefits 

of CDA enter the system are discussed. An end-to-end use case is described where a vehicle 

enters the freeway, engages in CDA features described in the prior chapters (including actions by 

both the CDA-enabled vehicle and roadway infrastructure using mapping and planning rules), 

and exits the freeway.

END-TO-END CDA OPERATIONS FROM FREEWAY ENTERING TO EXITING

This section describes possible CDA operations for a CDA vehicle from the time it enters a 

freeway from an onramp to when it exits the freeway using an off-ramp. This scenario is 

designed to cover all key features of IHP2, including CLF, lane coordination, and traffic 

management.

As shown in figure 10-A, CDA vehicle A aims to enter the freeway from the onramp. When 

vehicle A approaches the onramp, it receives information from the cloud that there are multiple 

vehicles nearby, and that two of them—vehicle B (a CDA vehicle) and vehicle C (a human 

connected vehicle)—will impact vehicle A’s trajectory of merging. In this scenario, vehicle A 

and vehicle B both belong to cooperation class C. Vehicle C is a human-driven vehicle and can 

share its own status with the cloud and with vehicles A and B. The negotiation is coordinated 

between vehicles A and B with the purpose of ensuring minimum disturbance to the mainline 

traffic. The result of the negotiation is for vehicle B to make a lane change to the left to create a 

gap for vehicle A to merge into the mainline. All other vehicles can be either nonconnected ADS 

vehicles or nonconnected human-driven vehicles. The nonconnected vehicles will perform 

perception and decisionmaking independently without any communication and data sharing with 

other vehicles. CDA vehicles will need to use onboard sensors to detect the nonconnected 

vehicles, regardless of the nonconnected vehicle’s status as ADS or human driven, and plan 

corresponding trajectories to avoid any conflict. 

In figure 10-B, the merge process of vehicle A has been completed. Because of the shared 

information of vehicle C, vehicle A is able to follow vehicle C closely and forms a short CACC 

string in the short term. In the meantime, as shown in figure 10-C, vehicle A negotiates with 

vehicle E, the leader of the platoon in front of vehicle A, to join the platoon from the rear. 

Vehicle A will be a platoon member until it reaches near the off-ramp as its destination.

After a certain period, vehicle A and the platoon approach another merge with reduced traffic 

performance because of heavy merge traffic. The cloud considers the necessity to perform CTM, 

including speed control/harmonization, and lane assignment. In figure 10-D, all platoon members 

are first instructed not to move to the right lane at or upstream of the bottleneck merge area

(geofenced). Second, the platoon leader also receives speed commands from the cloud to slow 

down to perform speed harmonization of the upstream traffic. Vehicle A, as a member of the 

platoon, receives commands directly from the cloud, but gives priority to the platoon leader and 

slows down with all other members of the platoon. 
36 

In figure 10-E, vehicle A informs the platoon leader, vehicle E, of its intent to leave the platoon. 

Vehicle E will provide instructions to vehicle A and the platoon member vehicles behind 

vehicle A to create larger gaps so that vehicle A could smoothly change lanes to the right. The 

lane change maneuver of vehicle A conflicts with planned trajectories of vehicle D, which 

intends to make a lane change to the left. Vehicle A identifies this conflict and negotiates with

vehicle D, which first slows down and then proceeds with making the lane change. These moves 

are parts of a multiparticipant cooperative maneuver. Vehicle A eventually reaches the 

destination off-ramp and completes the trip. Vehicle A benefits from multiple CDA features in 

this process, including cooperative merge, CACC, platooning, cooperative lane assignment, 

speed control, and cooperative weave. The combination of cooperation among vehicles and with 

the cloud not only improves individual vehicle travel experiences, but also enhances overall 

traffic system performance.

Source: FHWA.

A. Vehicle B moves to the left lane to facilitate the merge of vehicle A.

Source: FHWA.

B. Vehicle A forms a CACC string with human-driven vehicle C. 

Source: FHWA.

C. Vehicle A seeks to join the existing platoon in the left lane. 
37 

Source: FHWA.

D. Platoon vehicles remain in formation as they approach a bottleneck. 

Source: FHWA.

E. Vehicle D enters the platoon while vehicle A leaves the platoon.

Figure 10. Illustrations. End-to-end CDA operations. 

CTM WITH LANE ASSIGNMENT AND SPEED CONTROL

This section describes how TMCs (i.e., CARMA Cloud) use mapping and planning rules for 

downstream bottleneck reduction and queue management. This scenario describes how and what 

decisions are made from the cloud and TMC side. 

As illustrated in figure 10-D, mixed traffic of platoons, individual CDA vehicles, and 

human-driven vehicles are approaching a bottleneck area caused by heavy merge traffic. The 

cloud continuously monitors the traffic conditions by receiving information from all connected 

vehicles (CDA and connected human-driven vehicles) and infrastructure detectors, if any. The 

cloud fuses all received data and estimates that there is currently a long queue of stop-and-go 

traffic in the right lane and a slightly better condition in the left lane. The cloud runs the CTM

features to develop rules for speed, gap control, lane use, and other strategies to reduce the 

bottleneck conditions. 

First, the cloud implements speed rules by sending corresponding optimal speed commands to all 

CDA vehicles, including platoons, in geofenced areas. The purpose of the speed rules is to slow 

down the CDA vehicles and reduce the speed rate of the vehicles entering the bottleneck area. 

This process is also referred to as speed harmonization. Second, the cloud assigns the left lane as 

the desired lane for CDA vehicles and connected human-driven vehicles (via onboard devices) 

because the merge traffic from the right side of the onramp caused a long queue in the right lane. 

For vehicles that will exit the freeway via the next off-ramp, the cloud assigns the right lane as 

the desired lane early, before the vehicles are in the diverge or weave area. This lane use rule is 

expected to help improve the traffic flow for the freeway diverge or weave areas. As traffic 
38 

volume increases, the traffic flow density will become high, which makes it more difficult to 

change lanes approaching the off-ramp. When a vehicle is close to the exit but still cannot 

change lanes into the off-ramp, vehicles make aggressive lane changes to force their way into the 

target lane or to stop at certain points. This action potentially causes great disturbances in the 

traffic flow. If the lane assignment rules are used early and at the appropriate time, the vehicles

can start making lane changes at a greater distance upstream from the exit. In this case, a vehicle

will have a better chance to find an acceptable gap or negotiate with other CDA vehicles to 

create gaps for lane changes 

DEDICATED FACILITY OPERATIONS FOR EARLY DEPLOYMENT

This section describes a scenario in which a dedicated facility (i.e., managed lanes, dedicated 

ramps) is used for early deployment benefits. As illustrated in figure 11-A, the managed lane 

strategy has been widely used to serve high-occupancy vehicles (HOV), or drivers willing to pay 

a toll, to improve overall efficiency of the highway system. The CDA-dedicated managed lane 

can adopt a similar operational concept that only allows CDA vehicles to enter the managed lane. 

The managed lane physically separates the CDA traffic stream from regular traffic. As the CDA

vehicles concentrate in the managed lane, they will have a higher probability of traveling in 

vehicle platoons or CACC strings, thereby improving highway performance. It is also possible to 

allow special purpose human-driven vehicles (e.g., HOV vehicles) to use the managed lane in 

very early stages to make best use of the managed lane capacity. Simulation studies have 

demonstrated feasibility and system performance enhancement, even with mixed traffic (Ma et 

al. 2016).

Another possible dedicated infrastructure is the left-side dedicated ramp connected to the 

managed lane, as illustrated in figure 11-B. Because more vehicles will have access to the 

left-side managed lane as the CDA market penetration increases, vehicles entering the freeway 

from the right-side ramp may make multiple lane-change maneuvers to access the managed lane. 

This action creates a weaving section (shown as two dashed circles in figure 11-A representing

the start and end of the managed lane) that can reduce highway capacity and increase safety 

risks. Constructing dedicated ramps with direct access to the left-side managed lane can reduce 

such negative effects and can be considered as a part of the dedicated infrastructure strategy.

In this scenario, a limited-access, barrier-separated managed lane next to the median of a freeway 

with limited entry and exit ramps running alongside general-purpose lanes is considered. Only 

the CDA vehicles and connected human-driven vehicles are eligible to use the dedicated facility. 

Human single-occupancy vehicles (SOV) can also pay the tolls to use the managed lane. 
39 

Source: FHWA.

A. Managed lane without dedicated CDA ramp. 

Source: FHWA.

B. Managed lane and dedicated ramp.

Figure 11. Illustrations. Two dedicated facility operational scenarios. 

Congestion starts to form at multiple merging areas. The cloud monitors traffic flow conditions 

via roadside equipment and infrastructure-based sensors (e.g., radar traffic detectors), including 

traffic flow, density, and speed data, and transmits the information to the TMC. In addition, CDA 

vehicles transmit basic location and status (e.g., speed, direction) to the infrastructure devices, 

which then convey that information to the cloud for subsequent decisionmaking. The daily 

managed lane operations process for this scenario is as follows: 

• CDA and connected human-driven vehicles enter the managed lane at one end of the 

segment on dedicated ramps. CDA vehicles may form vehicle platoons based on onboard 

vehicle platooning algorithms subject to rules set by the cloud, such as maximum string 

length. Certain vehicles may seek to exit the facility at certain exit points. The vehicle 

platooning algorithm may also govern this process by, for example, splitting a long 

platoon into shorter platoons with the exiting vehicle at the end of one platoon and then 

letting the exiting vehicle disconnect itself from the platoon to exit via an off-ramp. If 

desirable, the two separate platoons can recombine once the exiting vehicle completes its 

departure.
40 

• The cloud aggregates, organizes, and summarizes streaming data received from 

connected vehicles and infrastructure-based systems. The cloud monitoring and detection 

systems identify flow breakdown events based on real-time received data.

• On the basis of current traffic demand and congestion level, the cloud will use existing 

pricing algorithms to calculate dynamic tolls and, via the infrastructure-based 

communications systems, send this updated information to all users to indicate that the 

cost of using the facility is changing. Some human SOVs may be discouraged from using 

the facility, thus reducing the demand. The reduced demand would improve the service 

quality for HOVs, CDA vehicles, and connected human-driven vehicles.

• On the basis of bottleneck traffic conditions, the cloud speed control/harmonization 

algorithm generates recommended speeds for all vehicles. Connected human-driven 

vehicles receive recommended speed commands for each freeway subsegment. CDA 

vehicles receive recommended speed profiles that are automatically followed.

• At merge areas, the cloud or roadside equipment receives merge requests from merge 

vehicles at the onramps. The cloud speed control algorithm generates speed commands 

for CDA vehicles on the managed lane for gap creation. The cloud cooperative speed 

control may also generate speed commands for the merge CDA vehicles to ensure the 

merging vehicles can use the created/assigned gaps to merge into the managed lane. 
41 

CHAPTER 5. ANALYSIS OF THE PROPOSED SYSTEM

This chapter provides an analysis of the benefits, advantages, limitations, and disadvantages of 

IHP2. A high-level system validation plan is also discussed. 

SUMMARY OF POTENTIAL BENEFITS AND OPPORTUNITIES

CDA technologies enable mobility applications that are not achievable by individual 

ADS-operated vehicles. Driving automation and connectivity present opportunities to deploy 

multiple cooperative automation strategies, but successful deployment depends on coordination 

among diverse stakeholders. 

CDA aims to improve the safety and flow of traffic and facilitate road operations by supporting 

the movement of multiple vehicles in proximity to one another. This result is accomplished by 

sharing information to influence DDT performance by one or more nearby road users. Vehicles 

and infrastructure elements engaged in cooperative automation may share information such as 

state and intent or seek agreement on a plan. Cooperation among multiple participants and 

perspectives in traffic can improve safety, mobility, situational awareness, and operations. 

For IHP2, three feature groups include CLF, CLC, and CTM. The three IHP2 feature groups not 

only include IHP1 features, but also cover additional features to present a comprehensive 

package for integrated freeway management with CDA. The concept of IHP is evolutionary, and 

new freeway applications not currently considered by researchers and developers are intended to 

be incorporated as they are conceived. Cooperation among vehicles and with the cloud not only 

improves individual vehicle travel experiences but also enhances the overall traffic system 

performance.

SYSTEM VALIDATION PLAN

This section describes system validation methods that will be used in the development of IHP2 

algorithms and software systems. The purpose of the validation testing is to ensure that the 

developed IHP2 system can meet all the operational needs listed in table 8 of chapter 3. 

Simulation Testing

The simulation can be designed to test IHP2 using the performance metrics identified in 

chapter 3 in terms of vehicle behavior and traffic system performance. Different types of 

simulation can be used and combined for testing purposes. 

ADS simulators offer the potential benefit of testing control algorithms embedded in real 

software because they support simulation of different vehicles with vehicle dynamics, sensor 

suites, environmental conditions, control software, and map generation that enable automated 

vehicle simulations. These capabilities enable more detailed testing of IHP2 software directly in 

the simulation environment under different critical scenarios. Performance metrics related to 

vehicle behavior can be extracted to understand if vehicle behavior meets the needs of the IHP2 

systems.
42 

Traffic simulators offer the possibility to scale up the evaluation to a highway corridor/network 

level (compared with the limited number of vehicles and length of the roadway for ADS 

simulators) to study the CDA impact on transportation system performance, as measured by 

traffic performance metrics such as safety, efficacy, stability, and sustainability. The traffic 

simulators can evaluate different scenarios, including various market penetration of CDA 

vehicles, traffic demand, various control strategies, and different infrastructure alternatives (e.g., 

dedicated ramps). Usually, the CDA control algorithms will be simplified from real software and 

will be parsimonious. However, calibrated/validated CDA behavioral models/algorithms (e.g., 

platooning vehicle following behavior as compared with the human driver following behavior) 

will be implemented for large-scale testing. 

Field Testing

Field testing can be conducted on a closed test track or selected open public road with different 

levels of live traffic. Depending on participation by partners, five or more CARMA vehicles 

loaded with IHP2 software can be instructed to run loops on the test track to represent continuous 

driving. End-to-end driving scenarios discussed in chapter 4 can be tested. These scenarios 

include cooperative merging, lane changing, joining a platoon, cooperative weaving, and CTM

(via sending rules such as speed and gap control to CDA vehicles). The purpose of testing is to 

collect vehicle behavior performance measures and validate whether the IHP2 software meets the 

needs established in chapter 4 of this report. 

Similar to IHP1 testing, IHP2 could also be tested on a public road with live traffic. Selected 

scenarios from the test track testing can also be applied. One key difference is the existence of 

live traffic, which will dynamically interact with all CDA vehicles. This testing would offer an 

opportunity to collect further data on human driver behavior in response to CDA vehicles. 

Data collected from both test track and public road testing can be used not only to calculate

vehicle behavior performance metrics, but also to calibrate traffic simulation CDA behavior 

models and human models in response to CDA behavior. This research can enable better 

validated evaluation of CDA’s traffic impacts in simulations. 

SUMMARY OF IMPACTS

IHP2 offers a holistic approach for optimally managing transportation systems and reducing 

disutilities. The benefits of IHP2 can only be realized when the cooperative control can be 

enabled by effective algorithms, including those for CLF, CLC, CTM, and a combination of 

different features. The need for controlling each individual CDA vehicle calls for highly scalable 

algorithms—possibly a mixture of distributed and centralized approaches—to guide all CDA 

vehicles in the transportation system. IHP2 presents changes to how TSMO is conducted. ITS 

infrastructure systems need to be upgraded to accommodate CDA system needs, such as cloud 

services and supporting information technologies. Agencies also need to evaluate and build 

capabilities for operating such systems. The conventional process of transportation system 

performance monitoring and reporting can be revolutionized with the prevalence of CDA 

vehicles and advanced sensors. 
43 

REFERENCES

Bujanovic, P. 2018. “Developing Vehicle Platoons and Predicting Their Impacts.” PhD 

Dissertation. University of Texas at Austin. 

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HRDO-10/09-22(WEB)E Recycled

Recyclable

Recommended Citation: Federal Highway Administration, 

Concept of Operations for CARMA Integrated Highway Prototype 2, 

(Washington, DC: 2022) https://doi.org/10.21949/1521866.