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

NHTSA · 2023 · ROSA P / United States. Federal Highway Administration. Office of Safety and Operations Research and Development

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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, which contribute to fuel consumption, travel delays, and collision risks. By enabling automated vehicles to communicate with smart traffic infrastructure, the study aims to demonstrate how CDA can support smoother navigation through intersections, thereby improving energy efficiency, reducing delays, and enhancing road safety. The methodology involved a two-phase evaluation process: simulation experiments and proof-of-concept (PoC) field testing. Initially, the research team conducted simulations to evaluate and fine-tune algorithms for four CDA cooperation classes defined by SAE International J3216. The implementation focused 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. 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, fuel consumption, and stopping time. Simulation data comparing human-driven vehicles with CDA-equipped vehicles (Level 3 automation, Class A cooperation) revealed that automated vehicles eliminate stop-and-go patterns and backward shock-wave propagation. Instead of coming to a complete stop, CDA vehicles adjust their speed ahead of yellow light changes or proceed through intersections at higher speeds during green intervals, resulting in smoother trajectories and reduced departure headways. The PoC testing confirmed that the framework met key objective metrics, including accurate estimation of intersection entry times and adherence to specified deceleration and acceleration boundaries. The study concludes that CDA applications in fixed-time signal environments offer significant benefits for traffic flow and efficiency. However, the authors note limitations in the current testing, particularly the lack of vehicle-to-vehicle communication and mixed-traffic environment validation. Future work is recommended to include large-scale testing with vehicle-to-vehicle communications, deployment in mixed-traffic environments where only some vehicles are equipped with CDA, and more dynamic scenarios involving lane changes, multiple vehicles, and vulnerable road users. These steps are identified as necessary to accelerate industry deployment and improve system reliability.

Key finding

Cooperative Driving Automation algorithms reduce average travel delay, fuel consumption, and stopping time at fixed-time signalized intersections by enabling vehicles to smooth their trajectories and avoid complete stops.

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