U.S. DOT Artificial Intelligence Research Highlights
Federal Aviation Administration
The FAA collaborates internally and maintains extensive partnerships across government, industry, and academia to develop integrated research plans that support the development of regulations, policies, procedures, guidance, and standards for drone operations. Research activities such as flight tests, modeling and simulation, technology evaluations, risk assessments, and data gathering and analysis provide the FAA with critical information in areas such as Detect and Avoid, UAS Communications, Human Factors, System Safety, and Certification, all of which enable the Agency to make informed decisions on safe drone integration.
Federal Highway Administration
On April 1, 2019, the Federal Highway Administration (FHWA) awarded a $4.9 million Advanced Transportation and Congestion Management Technologies Deployment (ATCMTD) grant to the Delaware Department of Transportation (DelDOT) for the Artificial Intelligence Integrated Transportation Management System (AIITMS) Deployment Program. AIITMS is a multi-modal AI transportation management and control system that collects and analyzes high-resolution data collected from freeways, traffic signals, and connected and autonomous vehicles. The system disseminates real-time travel information and generates traffic congestion solutions.
FHWA’s EAR Program has recently supported two research areas to develop technologies associated with artificial intelligence and machine learning. One area is the collection of large amounts of traffic data, including safety data, to spot trends and identify relationships between seemingly disparate data streams. The second area is the development of video analytics research to help determine driver behavior in various driving scenarios.
Federal Railroad Administration
The Federal Railroad Administration maintains several research investments into the use of machine learning and computer vision to improve railroad safety. Examples include the development of Robust Anomaly Detection for Vision-Based Inspection of Railway Components and other autonomous track inspection technologies