FDOT Project, BDV31-977-145: Project Summary – Evaluation of CARMA for I-STREET Testbed Implementation
The I-STREET research team conducted a thorough assessment of the Federal Highway Administration’s (FHWA) Cooperative Automation Research Mobility Applications (CARMA) program and on cooperative driving automation (CDA) capabilities for transit applications. Subsequently, they crafted a detailed guidance document to aid the Florida Department of Transportation (FDOT) in the seamless implementation of this technology in the I-STREET Living Lab.
Goal: The goal of the I-STREET research team was to deliver a comprehensive report offering guidance to the FDOT on how best to harness the potential of the CARMA program within the I-STREET Living Lab in Gainesville, FL.
Methodology: CARMA use cases, including the hardware used, were reviewed, and the status of development and testing was assessed; public and private sectors were consulted to understand the required components for a CDA-ready vehicle infrastructure system; test tracks and lab facilities were visited, which have used CARMA hardware
Key Findings: Based on an extensive review of hardware used by testing facilities and labs, six use cases were developed specifically for field implementation in the I-STREET Living Lab. The use cases were identified as cooperative perception (CP) applications, which are expected to improve the perception performance of automated vehicles.
Real-World Applications: The tailored use cases designed for implementation in the I-STREET Living Lab show significant promise for addressing the prevalent challenges related to pedestrian and bicyclist safety in Gainesville, FL. This guidance document furnishes FDOT with crucial details regarding technology readiness, a prioritized approach to problem-solving, and a clear financial roadmap for the successful integration of the recommended six use cases within the I-STREET Living Lab.
FDOT Project, BDV31- 977-117: Project Summary- Data Analytics & Evaluation of the Gainesville Trapezium Connected Vehicle Signal Phasing & Timing (SPaT) Deployment Project
Radio waves are the basis of most wireless communications, which can be long-range, such as cell phones, or short-range, such as Bluetooth. Dedicated short-range communications (DSRC) is another type of communication that “connected” vehicles can use to communicate with each other or with transportation infrastructure, e.g., traffic signals. Because these communications operate using different radio frequencies and different “languages,” they do not interfere with each other. DSRC can be used in systems that measure traffic flow and adjust signals in real time in response to heavier or lighter traffic, or to incidents such as crashes. In this way, traffic data transmitted via DSRC can help to improve the safety and efficiency of roadways. This requires rapid data transfer and fast processing to turn the data into useful information.
Research Objectives: University of Florida researchers evaluated the efficacy of connected vehicle (CV) technologies in improving efficiency and safety within a network of signalized intersections.
Project Activities: The researchers installed various sensing equipment, including loop detectors and video cameras, as well as a fisheye camera (upper right) that captures video at the intersection of 34th St and Archer Rd in Gainesville, FL, part of the Trapezium network used for this project. roadside units (RSUs) that collect and process data from the sensing equipment, along four connecting Gainesville roads and intersections called the Trapezium. The RSUs used DSRC to receive data from connected vehicles, which are equipped with on-board units (OBUs). The RSUs also sent traffic data from the traffic signals to the OBU where the data were displayed to aid the driver. The sensing technologies were the first layer of this system. In this project, the researchers focused on the second layer of the system that collects and processes the traffic data. The second layer consisted of the RSUs (edge computation), local servers, and cloud-based components, each of which performed specific data processing tasks. The researchers studied traffic behavior before and after the implementation of the CV system. The “before” study used existing data for crash statistics, operational studies of several intersections, and detector data to provide a baseline of both traffic operations and safety on seven Trapezium intersections. In the “after” study, the researchers collected traffic data from Trapezium sensors and the CVs to test the performance of the traditional and CV data collection pipelines to determine the efficacy of the CV system. Some comparisons were not possible because the before and after studies were impacted by the emergence of COVID-19, which had a strong effect on traffic patterns. However, many valuable observations were made, including the successful operation of the CV system and the responses of CV drivers. Drivers’ comments pointed researchers to many possible improvements in the system.
Project Benefits: Successful implementation of the CV system on the Trapezium is an important step in developing methods which can lead to safer and more efficient roadways.