Technology
New Drone-Based System Assesses Highway-Rail Crossings to Improve Safety
Poor highway-rail grade crossing design can contribute to rail incidents. To address this challenge, MTRI Inc. developed Crossing-i, a drone-enabled inspection system, to assess crossing safety with help from U.S. DOT’s SBIR program and the Federal Railroad Administration. Crossing-i can help rail...Robotic Utility Mapper Reduces Costs, Improves Safety During Infrastructure Planning and Maintenance
Accurately locating buried utilities is critical to safe infrastructure planning and maintenance. Enter RUMI—the robot that maps utility lines—developed by Intelligent Automation, Inc. with help from U.S.DOT's SBIR program and FHWA.First-of-Its-Kind Hazard Alert System for Motorcyclists
With support from U.S. DOT’s SBIR program, Charles River Analytics developed a first-of—its-kind roadway hazard alert application for motorcyclists that alerts motorcyclists to such roadway dangers as infrastructure hazards, surface hazards, roadway obstacles, and weather hazards.Highway-Rail Grade Crossing Safety and Trespass Prevention Research
The U.S. DOT Volpe Center’s Systems Safety and Engineering Division works on behalf of the Federal Railroad Administration’s (FRA) Highway-Rail Grade Crossing Safety and Trespass Prevention Program to evaluate methodologies, visual and audio warnings, motor vehicle and train presence detection,...Connected Vehicles at Highway-Rail Grade Crossings Research
The U.S. DOT Volpe Center’s Systems Safety and Engineering Division supports FRA research activities related to designing, developing, and testing the Railroad Crossing Violation Warning (RCVW) vehicle-to-infrastructure safety application for connected vehicles. This collection of publications and...Introducing Students to Careers in Intelligent Transportation Systems (ITS)
In 2014, FHWA sponsored an SBIR topic titled “STEM Education: Increasing Awareness about Intelligent Transportation Systems and Connected Vehicle Technologies for High School Students.” The topic aimed to create innovative, hands-on, problem-based learning opportunities that would give students the...Comparison of Two Community Noise Models Applied to a NASA Urban Air Mobility Concept Vehicle
InterNoise 2021
Abstract ID 1650
By Stephen A. Rizzi, NASA Langley, USA; Juliet A. Page, U.S. DOT Volpe Center, USA; and Rui Cheng, NIA, USA
Comparison of Two Community Noise Models Applied to a NASA Urban Air Mobility Concept Vehicle
CitationRizzi, Stephen A., Juliet A. Page, and Rui Cheng. Comparison of Two Community Noise Models Applied to a NASA Urban Air Mobility Concept Vehicle, Internoise 2021, August 2021.
AbstractPredictions of community noise exposure from a NASA urban air mobility (UAM) concept...