Office of Safety and Operations 

Research and Development 

1

Perception Sharing for Cooperative Driving Automation (CDA)

BENEFITS TO TRANSPORTATION

Cooperative perception improves the perception performances of automated 

vehicles and infrastructure, leading to enhanced situational awareness that could 

enable more effective safety and mobility CDA applications.(4) The enhanced 

situational awareness not only improves safety performance in immediate collision 

avoidance scenarios but also supports the 

automated driving system and CDA algorithms(4)

to better plan the vehicle’s path, speeds, and 

accelerations.(1) Moreover, enhanced perception 

could also support motion planning for improved 

mobility and energy performances.(4)

RESEARCH FOCUS

To test the CARMASM (5) cooperative perception 

system, the research team conducted a series of 

vulnerable road user scenarios(4) in which a 

pedestrian walked through a crosswalk at multiple 

angles with a stationary vehicle approaching the 

crosswalk. In some cases, the stationary vehicle had 

an obstructed line of sight, and in some cases it did 

not (figure 2).

In these scenarios, a roadside unit equipped with a 

thermal sensor was used for object detection and 

perception, CARMA StreetsSM (2)/Vehicle-toEverything (V2X) Hub(3) was used for information 

processing and communication, and a stationary 

vehicle running CARMA PlatformSM (6) was used to 

run the CARMA cooperative perception system.

Pedestrian and bicyclist fatalities make up about 19 percent of all traffic fatalities in 

the United States.(1) Both drivers and automated vehicles often have blind spots or 

limited sight lines where they cannot see vulnerable road users on the road. These 

visual limitations potentially contribute to those incidents.

Cooperative perception can help drivers and automated vehicles perceive vulnerable 

road users, other vehicles, obstacles on the road, and other objects by allowing 

vehicles and infrastructures to share the location data of nearby objects and 

additional helpful information with other roadway entities. For example, in figure 1,

 a roadside sensor can detect a pedestrian in a crosswalk and send nearby vehicles a 

message informing them of the vulnerable road user. Nearby vehicles can then 

determine if the pedestrian is a hazard and can provide a warning to their driver or 

perform an automated maneuver to ensure a crash is avoided.

Figure 1. Illustration. Cooperative 

perception example scenario.(2,3)

Source: Federal Highway Administration (FHWA).

Figure 2. Screenshots. Different vulnerable road user scenarios 

considering their intended path of travel and the location of the

vehicle receiving the information.

All images source: FHWA.

A. Test case 1. B. Test case 2.

D. Test case 4.

G. Test case 7.

C. Test case 3.

F. Test case 6.

I. Test case 9.

E. Test case 5.

H. Test case 8.

© Getty Images Plus.
COMMENDED CITATION: FEDERAL HIGHWAY 

MINISTRATION. PERCEPTION SHARING 

R COOPERATIVE DRIVING AUTOMATION 

DA1466

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EVALUATION OF THE CONCEPT

Independent evaluators from the U.S. 

Department of Transportation Volpe National 

Transportation Systems Center tested the 

CARMA(5) cooperative perception system

using the following criteria:

 Consistency and frequency of information

sent from one system to another.

 Latency between vulnerable road users’ 

movement, detection, creation, and

transmission of a personal safety message 

(PSM), and translation of PSM information 

into an object that the cooperative automated driving system (C-ADS) can plan for.

 Difference between the vulnerable road user positions estimated by the infrastructure sensors (figure 3) and the C-ADS

using LiDAR data (when both entities have clear lines of sight to the vulnerable road user).

PERCEPTION SHARING FOR COOPERATIVE DRIVING AUTOMATION

For more information, please contact the CDA Program at CDA@dot.gov

FHWA-HRT-24-026

HRSO-40/12-23([200])E

Recommended citation: Federal Highway Administration. Perception Sharing for 

Cooperative Driving Automation (CDA) (Washington, DC: 2023) https://doi.org/10.21949/1521466

RESULTS

The CARMA(5) cooperative perception system could successfully transfer and process information in less than 6.5 ms, on 

average, from the time a vulnerable road user was detected to when the detection was communicated to the C-ADS, 

allowing the system to make safety decisions faster.

NEXT STEPS

Future research could potentially focus on the following areas:

 The careful calibration of infrastructure-based sensors in the deployment environment was found by the research

team to be especially determinative in system efficacy. Achieving more accurate detection and perception could

prove to be an essential component in cooperative perception technology’s success.

 The standardization of sensor outputs of object detection and perception, especially standardized uncertainty

measures, would significantly accelerate cooperative perception technologies by enabling effective data fusion.

 Standardized sensor application programming interface would also significantly accelerate cooperative perception

technologies by enabling relevant software to work with different sensors from various vendors.

 Sensor fusion in the CARMA(5) ecosystem will be tested in simulation to analyze effectiveness.

 Adaptive communication protocols will be developed, so that message content and broadcast are adjusted based

on context (traffic situations).

REFERENCES

1. FHWA. 2023. “Pedestrian and Bicycle Safety” (web page). https://highways.dot.gov/safety/pedestrian-bicyclist, last accessed September 8, 2023.

2. FHWA. n.d. “carma-streets” (software and configuration files in GitHub repository). https://github.com/usdot-fhwa-stol/carma-streets, last accessed November 15, 2023.

3. FHWA. 2023. “V2X-Hub” (software and configuration files in GitHub repository). https://github.com/usdot-fhwa-OPS/V2X-Hub, last accessed April 13, 2023.

4. Lou, Y., A. Ghiasi, M. Jannat, S. Racha, D. Hale, W. Goforth, and P. Bujanovic. 2022. Cooperative Automation Research: CARMA Proof-of-Concept TSMO Use Case Testing: CARMA

Cooperative Perception Concept of Operations. Report No. FHWA-HRT-22-062. Washington, DC: Federal Highway Administration. https://www.fhwa.dot.gov/publications/research/operations/22062/22062.pdf, last accessed November 15, 2023. 

5. USDOT. n.d. “CARMA” (webpage). https://its.dot.gov/cda, last accessed May 30, 2023.

6. FHWA. 2023. “V2X-Hub” (software and configuration files in GitHub repository). https://github.com/usdot-fhwa-OPS/V2X-Hub, last accessed April 13, 2023.

TO LEARN MORE AND FOLLOW UPDATES

V2X GitHub

https://github.com/

usdot-fhwa-OPS/V2X-Hub

CARMA Platform

https://github.com/usdotfhwa-stol/carma-platform

Figure 3. Graph. Difference between projected and actual vulnerable 

road user distance from the infrastructure sensor (camera).

Source: FHWA.