As an integral piece in future safety, traffic flow, and energy improvements for the Nation’s transportation system, cooperative driving automation (CDA) technology facilitates the sharing of electronic messages between vehicles and equipped roadway infrastructure. Through its CARMA℠ Program, the Federal Highway Administration advances emerging capabilities in automation and cooperation by conducting research, testing, and deployment of CDA features.

Partnerships between the Intelligent Transportation Systems Joint Program Office, Federal Transit Administration, and Federal Motor Carrier Safety Administration support the development of cooperative driving systems aimed at improving basic traffic flow to reduce recurring congestion at traffic signals, intersections, and bottleneck areas. Further details on the CARMA Program can be found in the article “CARMA℠: Driving Innovation” in the Winter 2020 issue of Public Roads.

The CARMA Program includes three research tracks to facilitate targeted stakeholder participation in the goals of developing, testing, simulating, and deploying CDA solutions in order to prepare for these impacts:

• Research Track 1—Traffic: Focuses on normal travel and recurring traffic conditions that result in congestion on freeways and arterials.
• Research Track 2—Reliability: Focuses on nonrecurring congestion on freeways and arterials that is caused by work zones, inclement weather, traffic incidents, and other irregular instances.
• Research Track 3—Freight: Focuses on commercial motor vehicles operating in and around port facilities.

As defined by FHWA, transportation systems management and operations (TSMO) is a set of strategies that focus on operational improvements that can maintain and even restore the performance of an existing transportation system before extra capacity is needed. CDA provides an opportunity to coordinate with TSMO operational strategies and the surface transportation system as a whole. Under the CARMA Program, several use cases accounting for CDA-equipped vehicles, human-driven vehicles, equipped infrastructure, and the interactions between each are being researched.

“With innovations in CDA technology taking place every day, we are seeing that TSMO principles allow us to explore the future of our connected transportation system through the context of existing capabilities and solutions,” says Faisal Saleem, manager of the Intelligent Transportation Systems Branch at the Maricopa County Department of Transportation.

Understanding the Background 

All road users understand the complications posed by busy intersections, merging, and stop-and-go traffic. On the surface, these are seemingly unavoidable symptoms of a modern transportation system. But on a deeper level, these issues can increase traffic delays, escalate energy consumption and emissions, and interfere with safety. The good news is that developing technology can help address these issues. As part of an interconnected system rooted in vehicle-to-vehicle, vehicle-to-infrastructure, and infrastructure-to-vehicle communications, vehicles equipped with connected and automated driving systems have the potential to coordinate with one another and surrounding infrastructure to enhance safety, efficiency, and environmental benefits.

“In order to prepare for a more connected, safe, and efficient path forward, we must first understand how all of the pieces fit together,” says Jennifer Toth, director and county engineer at the Maricopa County Department of Transportation. “Surrounding infrastructure is as important as the vehicles themselves because we only see success when both work together.”

FHWA has developed several use cases to study the impact of CDA on TSMO strategies for basic travel.

Basic Travel Use Case: Merging 

Basic travel refers to recurring activities that transportation management services and traffic management centers will typically perform during normal operations when there are no incidents, special events, or other anomalies. A “notify and advise” operational action provides situational awareness to vehicles regardless of their automation level. The “merging” use case explores an automated vehicle merging onto a roadway and into the normal flow of traffic with other vehicles. Using cooperative lane change and unobstructed lane merge features on CDA-equipped vehicles, the CARMA Platform plans a smooth sideways motion from one lane into an adjacent lane, accounting for other automated and nonautomated vehicles through sensors on the vehicle.

Once the merge is complete, CARMA Cloud℠ (the cloud-based, open-source software systems enabling communication and cooperation between cloud services, vehicles, infrastructure, and road users) transmits geofence messages and speed commands to the first vehicle in line, which it then shares with the rest of the vehicles to ensure a successful transition to the destination and the eventual termination of the line of vehicles. Through using this framework and reducing the influence of human behavior, solutions for increased highway throughput and traffic shockwave mitigation become more attainable.

CDA solutions for road weather management, traffic incident management, and work zone use cases examining several operational actions are also in development in conjunction with the basic travel use case. All of these frameworks explore distinct actions that the entity determining TSMO activities must complete in order to execute TSMO strategies.

Proof-of-Concept Testing for TSMO Use Cases 

The CARMA proof-of-concept testing for TSMO use cases builds on previous research and leverages the CARMA Program ecosystem to enhance capabilities for CDA participants through four distinct arterial use cases. The aim is to reduce energy consumption and increase safety at multilane intersections. All vehicles in each use case were equipped with CDA technologies, and intersection infrastructure contained the necessary software and hardware to enable the two-way exchange, collection, and processing of electronic messages with all vehicles. CDA aims to improve the safety and flow of traffic by supporting the movement of multiple vehicles sharing the roadway in an increasingly automated future.

Simulations 

Researchers conducted a series of simulations for each use case including a human-driven vehicle as the baseline and four vehicles equipped with connected and automated driving systems, representing Society of Automotive Engineers (SAE) operational classes C and D. The results showed that CDA technology reduced average fuel consumption, wait and stopping times, and delays. Additionally, the results showed that the proposed roadside components working together can accurately and efficiently control vehicle operations, coupled with the vehicle intent information. The results demonstrated that, generally, these outcomes improve as the operational class of a vehicle increases.

TSMO Use Case 1 

The first TSMO use case is basic arterial travel—stop-controlled intersections. This use case has two main components, with the first, critical time step estimation (CTSE), designed to run on roadside equipment and the second, trajectory smoothing, on CARMA-equipped vehicles.

CTSE aims to estimate crucial points in a vehicle’s journey in an intersection, such as stopping at a stop sign and accelerating through an intersection. The CTSE is determined either by the roadside equipment or by the vehicle itself, depending on its SAE operational class. Trajectory smoothing, which aims to streamline vehicle paths, is determined by the vehicle itself.

In this use case, a vehicle enters the communication area, or intersection, joins the entering vehicle set, and begins transmitting information to roadside equipment and other vehicles. When the vehicle reaches the stop sign, it transitions to the ready-to-depart vehicle set. This transition is referred to as the stopping time, and the vehicle stores this information internally. Next, the vehicle proceeds to enter and navigate through the intersection, then exits and transitions to the discharging vehicle set, and eventually leaves the communication area entirely.

With CTSE and trajectory smoothing working together, the roadway infrastructure provides critical high-level input regarding when vehicles will perform specific actions, and enables vehicle control over trajectories and collision avoidance. The research team determined that the communication area could be expanded by adding more roadside equipment if needed to share real-time operational status (such as location and speed) and intents (such as movement group, stopping time, and entering time) among the intersection components.

TSMO Use Case 2 

The second TSMO use case is CDA optimization at fixed-time and actuated traffic signals. Like the first use case, this use case contains CTSE for roadside equipment and trajectory smoothing for vehicles as its main components. In this scenario, CTSE aims to estimate when a vehicle will enter an intersection, and trajectory smoothing seeks to streamline the vehicle’s trajectory by estimating the time of its entrance. When a vehicle enters the communication area, it will start to transmit information to roadside equipment and other vehicles, including its desired entry time. Then, the vehicle will plan its trajectory for passing through the intersection, either at the speed limit or the highest speed safely allowed. As it travels through the intersection, it will transition from the entering vehicle group to the discharging vehicle set, and eventually leave the communication area.

TSMO Use Case 3

The third TSMO use case is traffic signal optimization with CDA at signalized intersections, which deals with vehicles at intersections with traffic signals. To address this, the use case added a signal optimization component that runs on a centralized roadside server to support real-time vehicle information. Upon joining the entering vehicle set and gathering information from roadside equipment and other vehicles, the vehicle will estimate its entry time to the intersection to smooth its trajectory through the intersection. This use case explores solutions such as trajectory planning and smoothing for each stage of the vehicle’s journey through an intersection, including approaching an intersection with other vehicles, entering the intersection, and exiting the communication area.

TSMO Use Case 4 

The fourth TSMO use case is dynamic lane assignment along roadways with traffic signals. Along with CTSE and trajectory smoothing, this use case incorporates a signal- and lane-optimization component that employs a centralized roadside server and real-time vehicle information to assist with assigning vehicles to specific lanes. This use case primarily works to optimize traffic flow at signalized intersections and generate the best signal timing plans and lane control to increase traffic efficiency.

The four proof-of-concept TSMO use case frameworks explore how CDA can be used to reduce operational complexity and associated risks and liabilities for traffic operators. They also distribute the computational burden among several entities, which makes the system more suitable to support real-time information. Additionally, the combination of cooperation between vehicles and between vehicles and roadside equipment can enhance the performance of the traffic system and improve the travel experiences of individual vehicles. The frameworks have the potential to reduce stop-and-go traffic and backward shockwave, increase throughput, and maintain safety for individual vehicles at stop-controlled intersections in a driverless future.

Down the Road 

CDA technologies offer a wider range of possibilities for the connected transportation system than vehicles without CDA capabilities. The benefits of the ability to exchange messages with CDA-equipped vehicles offer the opportunity to enhance and improve upon current traffic control strategies. The four CDA TSMO use cases could significantly improve the traffic system and individual travel experiences, reduce stop-and-go traffic patterns, and maintain safety at conflict areas such as intersections and merge lanes.

“The world is already headed in a more connected, automated direction,” says Cynthia Jones, a project manager at DriveOhio. “Now is the time to figure out how to bring all the benefits of this technology to the public in the safest possible way.”

Looking ahead, the CARMA Program continues to research, test, and optimize basic travel use cases and TSMO strategies in addition to exploring use cases for cooperative perception, traffic incident management, road work management, and work zones.

“Today’s research will set the foundation for a more sustainable, automated, and enjoyable transportation experience for everyone,” says Raj Ponnaluri, a management engineer for the Florida Department of Transportation.

Higher levels of safety, efficiency, and reliability are on the horizon because of FHWA’s research and testing for realistic scenarios.

Taylor Lochrane is the CARMA program manager in FHWA’s Office of Operations Research and Development, leading the open source development and collaboration efforts of CARMA with partners and stakeholders. He earned B.S., M.S., and Ph.D. degrees in civil engineering focused on transportation from the University of Central Florida.

Govind Vadakpat is the CARMA technical program manager in FHWA’s Office of Operations Research and Development, currently managing several CARMA projects focused in the area of cooperative driving automation. He holds a doctorate in civil engineering from Pennsylvania State University and a master’s degree in civil engineering from the University of Wisconsin–Madison.

Nicole Paladeau is a communications specialist contractor at the Saxton Transportation Operations Laboratory, contributing to marketing and engagement projects. She earned a B.A. degree from the University of Mary Washington.

For more information, contact Taylor Lochrane at taylor.lochrane@dot.gov or visit https://highways.dot.gov/research/operations/CARMA.