Cooperative Driving Automation (CDA) at Stop-Controlled Intersections

NHTSA · 2024 · ROSA P / United States. Federal Highway Administration. Office of Safety and Operations Research and Development

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Summary

This report evaluates Cooperative Driving Automation (CDA) at stop-controlled intersections, addressing the inefficiency of current systems where vehicles cannot pass through simultaneously. The research aims to demonstrate how real-time vehicle-to-infrastructure communication can optimize trajectories, allowing vehicles to coordinate movements, minimize delay, reduce fuel consumption, and improve safety. By sharing digital messages regarding operating states and infrastructure rules, vehicles can smooth their trajectories and maximize intersection throughput. The study employed a two-phase methodology: simulation experiments and proof-of-concept (PoC) testing. Simulations were conducted on a typical four-way, stop-controlled intersection with varying lane configurations to evaluate and fine-tune algorithms across different CDA cooperation classes defined in SAE J3216. The implementation focused on Automation Level 3, Cooperation Class D, utilizing the CARMA ecosystem, which includes the CARMA Platform (vehicle-side), CARMA Streets, and the V2X Hub (infrastructure-side). Following simulations, the team conducted PoC testing with real vehicles and infrastructure on closed test tracks at the Turner-Fairbank Highway Research Center. Validation testing, led by the Volpe National Transportation Systems Center, assessed performance in critical edge cases, such as conflicting and non-conflicting vehicle directions, against criteria for communication, safety, mobility, and trajectory smoothness. Simulation results indicated that CDA algorithms significantly reduced average travel delay, fuel consumption, and stopping time compared to baseline human-driven scenarios. Trajectory plots revealed that CDA-equipped vehicles maintained smoother flows by proactively slowing down rather than stopping repeatedly, thereby improving general fuel economy. Table 1 data showed improved levels of service across various traffic volumes, with Class D cooperation maintaining stable flow longer than lower classes. PoC testing confirmed that the frameworks met most identified metrics and requirements, with no safety concerns raised during validation. The findings establish CDA as a viable method for enhancing intersection efficiency and energy performance. The report concludes that while the current framework is effective, future work should extend testing to mixed-traffic environments, scale deployments using vehicle-to-vehicle communications, and incorporate vulnerable road users to increase system reliability. This research serves as a foundational building block for systems that utilize digital vehicle and infrastructure messages to optimize traffic operations.

Key finding

Cooperative Driving Automation algorithms reduce average travel delay, fuel consumption, and stopping time at stop-controlled intersections while maintaining safety and meeting operational performance metrics in proof-of-concept testing.

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