FACT SHEET

September 2023

COOPERATIVE PERCEPTION 

AND CONTROL FOR TRAFFIC 

SYSTEM OPERATIONS

This cooperative system would 

smoothly integrate and process 

data inputs from multiple vehicles 

in realtime, optimizing the signal 

phase and the vehicle’s approach 

to the intersection.

Cooperative driving automation (CDA) technology promises to revolutionize traffic 

systems in the future. The Federal Highway Administration (FHWA) is paving the 

way to make this cutting-edge technology a reality with the CDA Research Program 

(previously known as the CARMASM Program) and associated studies.(1,2) See figure 1

for an example of testing being conducted through this program. Despite the promise 

of CDA innovation, unanswered questions still exist. 

CDA RESEARCH 

A lot of the research on CDA in the past 5–10 yr 

has identified the many benefits of and 

strategies for employing automated vehicles. 

But much of this work assumes perfect sensing 

and communication and accurate knowledge 

of surrounding traffic and vehicle states. Yet, 

CDA environments with mixed traffic are 

inherently difficult to fully observe and accurately 

predict, especially the behavior of human-driven 

cars. Noise and physical limitations (i.e., range 

and resolution) can inhibit the ability of 

sensors embedded in vehicles and roadside 

infrastructure to gather data.(3)

 This project’s 

researchers aim to create a cooperative system 

that integrates communication between vehicles 

and roadside infrastructure and helps manage 

these difficulties embedded in a mixed-traffic 

environment. This cooperative system would 

smoothly integrate and process data input from 

multiple sources in realtime to increase the 

certainty of a mixed-traffic environment.

For example, CDA environments with mixed traffic (i.e., human-driven and automated 

vehicles) remain a challenge. Mixed traffic is difficult to predict, and roadside 

infrastructure sensors for processing the information in automated vehicles may be 

limited. In the Exploratory Advanced Research (EAR) Program-sponsored 2.5-yr 

study “Cooperative Perception and Control for Freeway Traffic System Operations,” 

researchers at the University of California, Los Angeles (UCLA) and the University 

of Cincinnati are addressing the uncertainties of car and traffic movement with their 

innovative cooperative perception and control framework. This framework fuses 

various data sources provided by roadside and connected-vehicle sensors to aid the 

overall perception and coordination of autonomous driving.

Source: FHWA. 

Figure 1. Transportation systems management and operations testing scenarios. The arrows 

show the intended path of travel for each vehicle optimizing its approach to the intersection.(2)
FACT SHEET

COOPERATIVE PERCEPTION AND CONTROL 

FOR TRAFFIC SYSTEM OPERATIONS

September 2023 

EXPLORATORY ADVANCED RESEARCH

OVERVIEW OF EAR PROGRAM STUDY

The study has two phases, laboratory research 

and field experiments. 

Laboratory Research

For phase 1, which has already been completed, 

the research team developed a concept of 

operations, including background research, 

problems to address, key concepts, operational 

scenarios, and summary of potential impact. 

The researchers then focused on developing 

cooperative perception algorithms. Using 

a range of data provided by sensors from 

automated vehicles and roadside infrastructure, 

the team applied artificial intelligence (AI) 

and deep-learning techniques to predict the 

real-time state of a traffic system. Figure 2 

depicts the expanded perception range 

achieved by fusing multiple vehicle and 

infrastructure sensors of diverse characteristics 

(e.g., sensor types, sensor placement, sensor 

accuracy and precision). 

Figure 3 presents the architecture of the 

vehicle-to-everything (V2X) cooperative 

perception system, including metadata sharing, 

feature extraction, compression and sharing, a 

V2X vision transformer, and detection heads. 

This algorithm leverages attention mechanisms, 

a “spotlight system” that helps the test vehicle 

focus on the most important parts of an image 

or point clouds from other vehicles and/or 

infrastructure. The researchers developed 

additional algorithms that incorporate physicsinformed AI to fuse V2X detection results 

with car-following behavior models. These 

algorithms predict comprehensive vehicle states 

and trajectories for all vehicles, automated/

connected or not, in a mixed-traffic environment.

The research team then developed cooperative 

algorithms for vehicle and traffic control. Using 

a decisionmaking AI technique known as the 

fuzzy inference system (FIS), CDA vehicles were 

trained to interpret various inputs and take 

appropriate actions. Figure 4 shows an example 

of an FIS decisionmaking system for each CDA 

vehicle platooning and merging. 

Source: FHWA. 

LiDAR = light detection and ranging; AV = autonomous vehicle; Infra = infrastructure. 

Figure 2. (a) Illustration of sensor placement and sensor data view in a V2X environment and (b) illustration of the LiDAR point cloud.(4)
FACT SHEET

COOPERATIVE PERCEPTION AND CONTROL 

FOR TRAFFIC SYSTEM OPERATIONS

September 2023 

EXPLORATORY ADVANCED RESEARCH

The project team also created macroscopic and 

microscopic controls to ensure efficient traffic flow 

under high-density conditions. These controls 

included a platooning module for automated 

vehicle coordination, a cooperative merge 

module to streamline merges into mainline 

traffic, and a macroscopic speed harmonization 

module that regulates vehicle speed limits and 

platooning gaps. The speed harmonization 

module employs a reinforcement learning 

approach within a genetic fuzzy system (GFS) to 

ensure smooth traffic flow. A GFS is a “learning 

program” that improves itself over time, like 

how living things evolve through generations, 

and it makes decisions based on rules that are 

not strictly yes or no but have some room for 

gray area, just like humans do. This module 

was designed to work synergistically with the 

platooning and cooperative merge modules for 

optimal results.

The cooperative perception and control 

algorithms were then tested through simulations. 

Since the CDA freeway applications require 

traffic and vehicle performance evaluation, 

the team used multiple simulators jointly to 

evaluate these algorithms. The researchers used 

Simulation of Urban MObility (SUMOTM), an 

open-source traffic simulator, and Car Learning 

to Act (CARLA®), an open-source tool that 

supports the development, training, and 

validation of autonomous driving systems, for 

their testing.(5,6)

Field Experiments

Phase 2 includes additional field testing to better 

calibrate simulation models and verify and validate 

the performance of developed algorithms. The 

researchers will use two CDA vehicles that are 

equipped with advanced LiDAR and camera 

sensors at the UCLA smart corridor equipped 

with diverse infrastructure-based sensors, edge 

computers, and V2X communication capabilities.

CONTACT

For more information about this EAR Program 

project, contact Pavle Bujanovic, FHWA 

Office of Safety and Operations Research and 

Development, at 202-493-3271 

(email: pavle.bujanovic@dot.gov).

LEARN MORE

To learn more about the EAR Program, visit 

https://highways.dot.gov/research/researchprograms/exploratory-advanced-research/

exploratory-advanced-research-overview. 

Source: FHWA. 

STCM = spatial-temporal correction module; CNN = convoluted neural networks. 

Figure 3. Schematic overview of the developed V2X perception system.(4)
FACT SHEET

COOPERATIVE PERCEPTION AND CONTROL 

FOR TRAFFIC SYSTEM OPERATIONS

September 2023 

EXPLORATORY ADVANCED RESEARCH

REFERENCE

1

FHWA. 2022. “CARMA Program Overview” 

(web page). https://highways.dot.gov/research/

operations/CARMA?utm_source=hero, last accessed, 

January 25, 2023.

2Vadakpat, G., D. Chou, H. Park, and S. Vu. 2022. 

“Cooperative Driving Automation (CDA) Research 

Program: Development, Testing, and Evaluation.” 

Public Roads 86, no. 3. https://highways.dot.gov/

public-roads/autumn-2022/04, last accessed, 

January 25, 2023. 

3Li, T., X. Han, and J. Ma. 2021. “Cooperative 

Perception for Estimating and Predicting 

Microscopic Traffic States to Manage Connected and 

Automated Traffic.” IEEE Transactions on Intelligent 

Transportation Systems 23, no. 8: 13694–13707.

4Xu, R., H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma. 

2022. “V2X-ViT: Vehicle-to-Everything Cooperative 

Perception with Vision Transformer.” Presented at 

European Conference on Computer Vision. Tel Aviv, 

Israel: Springer Science+Business Media.

5Eclipse Foundation. n.d. “SUMO” (website). https://www.

eclipse.org/sumo/, last accessed January 30, 2023.

6CARLA. n.d. “CARLA” (website). https://carla.org/, 

last accessed January 30, 2023.

Recommended citation: 

Federal Highway Administration, 

Cooperative Perception and Control for Traffic 

System Operations (Washington, DC: 2023) 

https://doi.org/10.21949/1521436

FHWA-HRT-23-105

HRTM-20/09-23(WEB)E

What Is the EAR Program?

The EAR Program supports longer term, 

higher risk research with the potential for 

transformative improvements to the U.S. 

transportation system. The EAR Program 

seeks to leverage promising expertise 

and advances in science and engineering 

to create breakthrough solutions to 

highway transportation issues. 

Source: FHWA. 

SDA = self-driving agent.

Figure 4. Schematic of the FIS decisionmaking system for each CDA to perform platoon and merge.

Inputs Actions 

Local information obtained from sensors of the 

CDA. Actions could include: 

e.g., Speed of the SDA, speed of vehicles close-by, 

and length of platoons. 

Information shared between CDAs. 

e.g., Intent to merge, intent to slow down, and 

intent speed up. 

Centralized information available from the 

cloud. 

Change lane, 

Stay in lane, 

Accelerate, 

Brake, and 

Merge to a platoon. 

U.S. Deportment of Tronsportotion 

Federal Highway Administration