Optimizing Vehicle Trajectories at Fixed-Time Traffic Signal Intersections Using Cooperative Driving Automation (CDA)

NHTSA · 2025 · ROSA P / United States. Department of Transportation. Federal Highway Administration

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Summary

This report evaluates the application of Cooperative Driving Automation (CDA) to optimize vehicle trajectories at signalized intersections with fixed-time traffic signals. The research addresses the inefficiencies and safety risks associated with traditional stop-and-go traffic patterns, noting that nearly 30% of motor vehicle fatalities in the United States occur at intersections. By leveraging vehicle-to-infrastructure communication, the study aims to demonstrate how automated vehicles can navigate these intersections more safely and efficiently, reducing delays, energy consumption, and backward shock wave propagation. The methodology involved a two-phase approach: simulation experiments followed by proof-of-concept (PoC) field testing. Initially, the Federal Highway Administration (FHWA) research team conducted simulations to evaluate and fine-tune algorithms for four CDA cooperation classes defined by SAE International J3216. The implementation focused specifically on Cooperation Class A (status sharing) within the CARMA ecosystem, utilizing the CARMA Platform and Vehicle-to-Everything (V2X) Hub. In this architecture, roadside units broadcast Signal Phase and Timing (SPaT) messages, while vehicles share status information via Basic Safety Messages and customized mobility messages. Following simulations, the team conducted multiple rounds of PoC testing using full-sized FHWA vehicles on controlled test tracks at the Turner-Fairbank Highway Research Center. These tests assessed communication, safety, mobility, and trajectory smoothness in critical scenarios, such as vehicle arrival at the beginning or end of green light intervals. The results indicate that the developed algorithms successfully reduce average travel delay, energy consumption, and stopping time. Simulation data revealed that CDA-equipped vehicles (Level 3 automation with Class A cooperation) exhibit smoother trajectories compared to human-driven vehicles, slowing down gradually before intersections rather than coming to complete stops. This approach eliminates stop-and-go patterns and reduces backward shock-wave propagation. Field testing confirmed that the PoC frameworks met key objective metrics regarding message processing, communication rates, and algorithm logic, including accurate estimation of intersection entry times and adherence to deceleration boundaries. Potential benefits include up to a 30% improvement in energy efficiency and a 50% reduction in stop time for road users. The study concludes that CDA applications significantly enhance performance at fixed-time signalized intersections, providing a foundation for future development. However, the current implementation did not fully test vehicle-to-vehicle communication due to platform limitations. The authors identify several areas for future work, including larger-scale deployments to increase reliability, testing in mixed-traffic environments where only some vehicles are equipped with CDA, and evaluating more dynamic situations involving lane changes, multiple vehicles, and vulnerable road users. These steps are deemed necessary to accelerate industry deployment and further validate the technology’s benefits.

Key finding

Cooperative Driving Automation algorithms reduce average travel delay, energy consumption, and stopping time at fixed-time signalized intersections by enabling smoother vehicle trajectories.

Methodology

mixed_methods

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• v2x connected vehicle