EAR Program Research Results - Updated Through 2018 : [brochure]

NHTSA · 2018 · ROSA P / United States. Federal Highway Administration

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Summary

The Federal Highway Administration’s Exploratory Advanced Research (EAR) Program addresses the need for high-risk, long-term research capable of delivering transformative improvements to highway transportation systems. Motivated by the necessity to make the U.S. highway system safer, more durable, and more efficient, the program leverages advances in science and engineering across five focus areas: connected highway and vehicle systems, materials science, human behavior, performance assessment technology, and energy conservation. Through 11 solicitations, the EAR Program funded 88 projects involving government and academic researchers, investing $88 million in federal funds and leveraging $28 million in matching funds. The program does not fund commercialization but aims to generate fundamental insights, new models, and prototypes that accelerate applied research and deployment. The research employed diverse methodologies, including hardware-in-the-loop simulations, field operational tests, and advanced modeling. In connected vehicle research, teams developed simulation platforms like CONVAS, which merges traffic simulation software with wireless communications models to test connected vehicle applications in real-time. Researchers also utilized hardware-in-the-loop systems with real engines to precisely measure fuel consumption and emissions for connected and autonomous vehicles. Field tests were conducted for cooperative adaptive cruise control (CACC) in truck platoons, using two- and three-truck formations on public highways and test tracks to evaluate aerodynamic benefits and driver behavior. Additionally, researchers developed advanced vehicle tracking systems using ultrawideband radar to monitor individual vehicles without relying on onboard vehicle technology. Key findings demonstrate significant potential for efficiency and safety improvements. CACC-equipped truck platoons achieved peak fuel savings of 5 percent for lead trucks and over 10 percent for trailing trucks, with fleets averaging more than 500 miles per trip yielding the highest return on investment. Simulations indicated that a 60 percent market penetration of CACC in freight trucks could improve overall traffic flow. Advanced traffic signal control algorithms, such as the DARE system, achieved over 95 percent accuracy in detecting collision risks, while in-vehicle speed advisories reduced fuel consumption by more than 13 percent in field tests. In materials science, research characterized novel cementitious materials and bio-based binders to extend concrete service life and reduce environmental impact. For accessibility, wearable navigation aids integrating GPS, inertial sensors, and computer vision were developed to assist visually impaired pedestrians in environments where GPS data is unreliable. The significance of these results lies in their potential to leapfrog current technological limitations and enhance the safety and reliability of the national transportation infrastructure. The findings provide a foundation for deploying connected vehicle technologies, such as autonomous intersection management and cooperative adaptive cruise control, which can reduce travel times, fuel use, and emissions. The development of new materials and sensor technologies supports the maintenance and sustainability of transportation assets. By validating these concepts through rigorous simulation and field testing, the EAR Program facilitates the transition of early-stage innovations into practical applications, ensuring the U.S. maintains a competitive and efficient transportation system for decades to come.

Key finding

The document serves as a comprehensive catalog of completed and ongoing research projects funded by the FHWA Exploratory Advanced Research Program, highlighting technological prototypes and simulation models rather than a single unified experimental result.

Methodology

review

Provenance

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

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• perceptual countermeasures

Information type

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• Methodological Resource: tool software, dataset resource, validation psychometrics