FHWA is conducting several ongoing projects developing advanced materials for use in highway infrastructure.

Spotlight Project: Inorganic Polymers: Novel Ordinary Portland Cement-Free Binders for Transportation Infrastructure

Producing conventional Portland cement can be energy intensive because cement manufacturing requires high heat. To address this concern, researchers at the University of California, Los Angeles (UCLA) developed cement-free inorganic polymer binders (IPBs) for producing concrete. These researchers studied how fly ash—a byproduct of coal-fired power plants—can replace Portland cement binders. With support from a variety of partners, including the EAR Program, researchers have defined fly ash’s characteristics at the atomic level to understand how those characteristics influence binder behavior during concrete production.

Image source: FHWA.

Connected Systems research focuses on applications of machine learning to advance the capabilities of traffic control systems to include multiple scales and different modes.

Spotlight Project: Preparing Our Nation’s Roadways for Advanced Vehicle Technologies

The researchers will use an intersection control system to transmit a time reservation to fully automated vehicles and use augmented reality and a heads-up display, such as a projection on the windshield, to notify the human drivers to either speed up or slow down to drive through the intersection at their appointed time. Read more at https://www.fhwa.dot.gov/publications/research/ear/20049/index.cfm  and at https://www.fhwa.dot.gov/publications/research/ear/18032/index.cfm.

Image source: Volpe, 2014

Advanced research in Human Behavior has recently invested in computer vision as a tool for safety and operations professionals to harness the large amount of imagery data while preserving privacy.

Spotlight Project: Automated Video Processing Algorithms to Detect and Classify High-Level Behaviors with Speed and Accuracy

FHWA has identified the detection and classification of high-level behaviors in video data as a gap in developing practical automated image processing to enhance the value of large research datasets and applications. To address this gap, the research team will first define behavioral semantics that can be identified by existing algorithms and then will link these semantics with high-level behaviors through machine learning, propagation of movement using intent-based cost models, and statistical relationships. This will allow future researchers to focus on analysis of driver behavior to improve traffic safety rather than building underlying computer vision tools.

Image source: Copyright Istock, 2007

FHWA is exploring new technology for assessing infrastructure performance using wild-life tracking.

Spotlight Project: Wildlife Crossing: Web-Connected Cameras Offer Improved Monitoring for Highways

FHWA supported research to increase the value of wildlife camera systems.  Working with researchers at University of California Davis and with State Departments of Transportation, the research began with automating the wireless transfer and sharing of image data of wildlife at culverts and overpasses that allow safe movement of wildlife across roadways.  The research continued with machine vision techniques that could automate the detection and labelling of animals in the images allowing ecologists to focus on the effectiveness of different designs to reduce wildlife vehicle conflicts rather than looking at images.

The fact sheet is located at https://www.fhwa.dot.gov/publications/research/ear/16033/index.cfm.

Image source: National Park Service, 2020