Office of Safety and Operations 

Research and Development 

© Getty Images.

Cooperative Driving Automation (CDA) 

at Stop-Controlled Intersections

BENEFITS TO TRANSPORTATION

This research project aims to demonstrate the benefits of CDA use at stop-controlled intersections.(1) This research 

provides an initial building block toward systems receiving digital vehicle messages that provide the real-time 

information and planned trajectory of the vehicle and sharing digital infrastructure messages that represent basic 

signage, rules of the road, or instructions for vehicles. In this research project, vehicles broadcast real-time information 

about the vehicles’ operating states, communicate directly with infrastructure to coordinate movement through 

stop-controlled intersections, and receive advisory messages from the infrastructure that help the vehicles smooth 

their trajectory, thus improving traffic safety, throughput, and energy efficiency.

Stop-controlled intersections help vehicles avoid 

potential crash risks and extremely high accelerations 

or decelerations. Currently, multiple vehicles cannot 

pass through a stop-controlled intersection at the 

same time. However, with available real-time vehicle 

information, vehicles can optimize their trajectories to 

be aware of other vehicles, plan accordingly to arrive 

and leave intersections at the best time from the stop 

line, and pass through the intersection simultaneously 

with other vehicles that do not have conflicting 

directions. Figure 1 shows a four-way, stop-controlled 

intersection and the technology the intersection uses. 

With the available real-time vehicle information, 

vehicles can also minimize delay and wait times, 

maximize safety, decrease energy consumption, and 

improve riding comfort.

© Getty Images Plus.

Figure 1. Graphic. Four-way, multilane, stop-controlled intersection.

Source: FHWA.
2

CDA AT STOP-CONTROLLED INTERSECTIONS

Source: FHWA. Source: FHWA.

Figure 3. Graph. Automation Level 3, cooperation 

Class A vehicle trajectory plots.(2)

Figure 2. Graph. HV trajectory plots.

Figure 4. Graph. Automation Level 3, cooperation 

Class D vehicle trajectory plots.(2)

Source: FHWA.

EVALUATION OF THE CONCEPT

The research team first conducted simulation experiments to evaluate and fine-tune the developed algorithms. The 

team conducted simulations at a typical four-way, stop-controlled intersection with two lanes on the eastbound and 

westbound approaches and a single lane on the northbound and southbound approaches. Simulation results show 

that the developed algorithms reduce average travel delay, fuel consumption, and stopping time at stop-controlled 

intersections.(1) The results demonstrate that with reduced stopping time and less stop-and-go traffic, general fuel 

economy will improve with CDA and will improve even more when vehicles can optimize departure sequence and 

utilize intersection resources to maximum capacity with CDA. This enhancement also leads to less average delay, 

highlighting the reduction in stop time during travel for the cases illustrated in figure 2 through figure 4. Table 1 

displays the improvement in the level of service at stop-controlled intersections for different CDA cooperation class 

scenarios compared to the baseline human-driven vehicle (HV) scenario.
3

Figure 2 through figure 4 illustrate vehicle trajectories with space-time motion plots for a selected lane and simulation 

run. The vertical axis represents space in meters, while the horizontal axis represents time in seconds. In these graphs, 

each solid line corresponds to the trajectory of a single vehicle, and a change in the line’s slope directly correlates to a 

change in the vehicle’s speed. For example, as the slope of the line increases, the speed of the vehicle also increases 

and vice versa. These visuals highlight a crucial observation; unlike HV, which might have to stop at the stop bar 

multiple times before entering the intersection, vehicles equipped with Level 3 Class A CDA technology demonstrate a 

smoother flow by proactively slowing down.(2) Additionally, figure 4 underscores the significance of optimizing the 

departure sequence of vehicles with Level 3 Class D CDA technology.(2) This optimization maximizes intersection 

resources, leading to a substantial reduction in waiting times at stop-controlled intersections.

The research team then performed several levels of proof-of-concept (PoC) testing with real vehicles and 

infrastructure on closed test tracks. The defined acceptance criteria included various operational aspects such as 

communication, safety, mobility, and trajectory smoothness. The team installed algorithms on FHWA vehicles and 

infrastructure and independently tested those algorithms.(1) All testing took place at the Turner-Fairbank Highway 

Research Center and evaluated system performance in critical edge case scenarios, such as two vehicles with 

conflicting directions or two vehicles without conflicting directions. Validation testing was led by the U.S. Department 

of Transportation Volpe National Transportation Systems Center. Analyses of the verification and validation testing 

show that the PoC frameworks met most of the metrics and requirements identified in in the test plans.

USE CASE ARCHITECTURE

While the algorithms were developed and simulations were conducted for different CDA cooperation classes defined 

in SAE International® J3216TM, the implementation of this use case on the CARMASM ecosystem focuses solely on 

automation Level 3, cooperation Class D.(3,4) The components of the CARMA ecosystem used in this cooperation class 

include CARMA PlatformSM, CARMA StreetsSM, and Vehicle-to-Everything (V2X) Hub. (See references 4, 5, 6, and 7.) 

Figure 5 shows how each aspect of CARMA infrastructure works together. CARMA Streets and the V2X Hub reside 

within the infrastructure and are jointly responsible for processing the information received from vehicles and 

scheduling those vehicles by calculating their stopping and entry times.(6,7) Stopping time refers to the time a vehicle 

takes to decelerate and come to a full stop, while entry time is the time a vehicle takes to cross into the intersection. 

CARMA Platform is situated within the vehicle and receives advisory messages from the infrastructure, controlling 

vehicle trajectories to minimize stopping time and optimize vehicle energy and fuel efficiency.(5) 

CDA AT STOP-CONTROLLED INTERSECTIONS

Figure 5. Graphic. CARMA design and architecture for stop-controlled intersections use case—automation Level 3, 

cooperation Class D. (See references 3, 5, 6, 7, and 8.)

Source: FHWA.

Table 1. Level of service at the stop-controlled intersection.(2)

Vehicle Type or Level 3 Automation Cooperation Class Scenarios

Vehicle Type or Level 3 

Automation Cooperation 

Class Scenarios(2)

600 720 840 960 1,080 1,200 1,320 1,440 1,560 1,680 1,800

CLASS A A B C E F F F F F F F

CLASS B A A A A A A A C D E F

CLASS C A A A A A A A A B C D

CLASS D A A A A A A A A B B C

A = free flow; B = reasonably free flow; C = stable flow, at or near free flow; D = approaching unstable flow; E = unstable flow, operating at capacity; 

F = forced or breakdown flow.

BSM = basic safety message; MOM = mobility operations message; MPM = mobility path message; Json = JavaScript Object Notation.
TITLE OF TECHBRIEF

4

Extend this test case to a 

mixed-traffic environment, where 

only part of traffic is equipped with 

CDA technology.

Continue to apply vehicle-to-vehicle 

communications to enable higher 

scale deployments and further 

examine the benefits demonstrated 

in simulation experiments.

Add consideration of vulnerable 

road users to the system to increase 

reliability and accuracy.

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

FHWA-HRT-24-030

HRSO-40/12-23(200)E

Recommended citation: Federal Highway Administration. Cooperative Driving 

Automation (CDA) at Stop-Controlled Intersections (Washington, DC: 2023)

https://doi.org/10.21949/1521470

CDA AT STOP-CONTROLLED INTERSECTIONS

REFERENCES

1. Soleimaniamiri, S., X. S. Li, H. Yao, A. Ghiasi, G. Vadakpat, P. Bujanovic, T. Lochrane, J. Stark, S. Racha, and D. Hale. 2022. FHWA Cooperative Automation Research: 

CARMA Proof-of-Concept Transportation System Management and Operations Use Case 3–Traffic Signal Optimization with CDA at Signalized Intersections. Report No. 

FHWA-HRT-22-052. Washington, DC: Federal Highway Administration.

2. SAE International. 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. J3016_201806. Warrendale, PA: 

SAE International. https://www.sae.org/standards/content/j3016_201806, last accessed May 30, 2023.

3. SAE International. 2020. Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles. SAE J3216_202005. Warrendale, 

PA: SAE International. https://www.sae.org/standards/content/j3216_202005/, last accessed October 19, 2020.

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

5. FHWA. n.d. “carma platform” (software and configuration files in GitHub repository). https://github.com/usdot-fhwa-stol/carma-platform, last accessed April 12, 

2023.

6. FHWA. 2023. “carma streets” (software and configuration files in GitHub repository). https://github.com/usdot-fhwa-stol/carma-streets, last accessed April 12, 2023.

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

8. JavaScript Object Notation. n.d. “Introducing JSON” (web page). https://www.json.org/json-en.html, last accessed October 4, 2023.

9. SAE International. 2023. V2X Communications Message Set Dictionary. J2735_202007. Warrendale, PA: SAE International. https://www.sae.org/standards/content/

j2735_202007/, last accessed November 27, 2023.

CONCLUSION

This test case proved the benefits of CDA application in stop-controlled intersections. While the research team identified 

some limitations through data collection and analysis, the results raised no safety concerns and the potential for future 

work remains high. In particular, the developed framework can be improved in the following three ways:

TO LEARN MORE AND FOLLOW UPDATES

CARMA Streets

https://github.com/usdot-/fhwastol/carma-streets

CARMA Platform

https://github.com/

usdot-fhwa-stol/carmaplatform

CDA COOPERATION CLASSES 

Defined in SAE J3216 Standard(3)

 Class A: Status Sharing 

 Class B: Intent Sharing 

 Class C: Agreement Seeking 

 Class D: Prescriptive 

STANDARDS

This technology meets the following standards established by SAE International:

 SAE J3216_202107: Taxonomy and Definitions for Terms Related to CDA for On-Road 

Motor Vehicles.

(3)

 SAE J3016_202104TM: Taxonomy and Definitions for Terms Related to Driving 

Automation Systems for On-Road Motor Vehicles.

(2)

 SAE J2735_202007TM: Vehicle-to-Everything Communications Message Set Dictionary.

(9)