EAR Program Research Results: Updated through 2016

NHTSA · 2016 · ROSA P / United States. Federal Highway Administration

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Summary

This document summarizes the research outcomes of the Federal Highway Administration’s (FHWA) Exploratory Advanced Research (EAR) Program through 2016. The EAR Program addresses the need for high-risk, long-term research capable of generating breakthrough improvements in highway safety, efficiency, and environmental sustainability. By leveraging advances in science and engineering, the program funds projects that might not otherwise receive support, focusing on connected vehicle systems, materials science, human behavior, performance assessment, and energy conservation. As of September 2016, the program had awarded 79 projects totaling $76 million in FHWA funds, leveraging an additional $28 million in matching funds. The research employs diverse methodologies, including simulation, field testing, laboratory analysis, and data analytics. In connected vehicle systems, researchers utilized simulations and robotic vehicles to test autonomous intersection management (AIM) and advanced traffic signal controls. For materials science, experiments focused on concrete durability, incorporating hybrid fibers, fly ash, and carbon nanofilaments to enhance structural integrity and reduce environmental impact. Human behavior research relied heavily on the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study, analyzing 1.2 million hours of video and 2,000 terabytes of data from over 3,400 drivers using machine learning and computer vision to automate feature extraction and driver coding. Key findings demonstrate significant potential for improving transportation systems. Autonomous intersection management algorithms were shown to improve intersection efficiency and reduce congestion. Advanced traffic signal strategies, such as dynamic all-red extensions, achieved over 95 percent detection accuracy for collision risks, while in-vehicle speed advisories reduced fuel consumption by more than 13 percent. In freeway merging, a merge-control algorithm increased average speed by 23.6 percent and decreased travel time by 11.5 percent, with benefits emerging at 50 percent vehicle connectivity. Materials research confirmed that hybrid fiber-reinforced concrete significantly improves corrosion control, while high-volume fly ash concrete maintains strength comparable to conventional mixes. Additionally, automated pedestrian detection systems achieved a 90 percent positive detection rate under good visibility, and automated driver coding systems successfully extracted behavioral features from large video datasets. The significance of these results lies in their potential to transition into practical applications that enhance safety, reduce emissions, and extend infrastructure lifespan. The findings suggest that connected vehicle technologies can yield substantial benefits even at low market penetration rates, particularly in congested networks. Furthermore, the development of durable, eco-friendly concrete materials offers a pathway to greener highways with reduced maintenance needs. By providing fundamental insights and prototypes, the EAR Program supports the broader research community in advancing next-generation transportation solutions, ensuring the U.S. maintains a safe, efficient, and environmentally sound highway system.

Key finding

Connected vehicle signal control strategies achieved fuel savings of over 13 percent in field tests, while autonomous intersection management algorithms dramatically improved intersection efficiency and reduced traffic delays.

Methodology

review

Provenance

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Topics

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• perceptual countermeasures
• exposure measurement
• situational awareness

Information type

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• Methodological Resource: tool software, dataset resource
• Theoretical Contribution: computational model