WASHINGTON, D.C. -- US Ignite today announces the launch of a smart transportation testbed and autonomous vehicle (AV) pilot program in coordination with Fort Carson, neighboring Colorado Springs, and the University of Colorado’s Research and Engineering Center for Unmanned Vehicles (RECUV). With a focus on reducing transportation costs, improving public safety, and delivering faster services, the $4 million joint endeavor – funded by the U.S. Army Engineer Research and Development Center (ERDC) – is designed to serve as a model for future smart military installations nationally.
Out of an annual transportation budget of $1 billion, the Department of Defense (DoD) spends $435 million yearly for non-tactical passenger vehicles and light trucks, with a use rate of just 7%. This puts the DoD in a unique and urgent position to leverage new technologies that could significantly reduce vehicle spending.
In addition, with a large percentage of service members living off base, the development of new sensor-driven and AV-related technologies has the power to improve road safety and decrease congestion not only within military installations, but also in the cities and towns that surround them.
Phase one of the Fort Carson program will begin with the deployment of up to two automated shuttles on site. As the program develops, the research team will also explore on and off-post automated delivery vehicles and shuttles.
"The future of smart military installations, and of transportation technologies, is dependent on implementing high-quality, data-driven, and repeatable pilot programs that enable researchers to investigate challenges securely and at scale,” said Nick Maynard, Chief Strategy Officer, US Ignite. "We envision the Fort Carson project as the start of a wave of new research testbeds at military posts that will help revolutionize transportation for decades to come."
The program will also include a sophisticated data sharing initiative between Fort Carson and Colorado Springs. Smart sensors will be used on base to monitor traffic, parking, and public safety, and will be linked to information from the city’s own sensors and mapping systems to create joint data repositories. Researchers will apply analytics to these datasets to improve safety and services, and eventually to develop machine-learning models that prioritize transportation resources based on usage rates and community needs.