Department of Transportation

The ART Network is the shared service environment for AI research and development activities that provides researchers with access to a rapid AI innovation, exploration, development, and sharing platform using secure...

Provides operational AI use case developers with access to shared environments with all OCIO-cleared AI functionality for use case experimentation, development, and initial data and model risk management identificatio...

The TrAIN is the set of shared service environments for operational AI use cases that provides AI developers with a pre-established, secure AI-enabled environments for development, testing and deployment under the Chi...

Will strengthen the Intake and Assignment Branch's precision of FOIA request assignments at the first take, freeing up time to actively engage with requesters and customers alike.

DOT needs access to multi-model environment that allows to experiment with and compare top AI models.

Automated review of accident narratives.

to label radio communication transcripts according to the HP Taxonomy labeling system.

The AI/ML tool helps detect and predict where precipitation currently is and where it will be at the future at various altitudes in parts of the NAS that do not have traditional ground based weather radar.

This Compliance Plan conveys DOT's approach to achieving consistency with OMB Memorandum M-24-10 Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence.

The AISCC provides all DOT employees and contractors with a website for getting educated on AI topics, get inspired by DOT and external use cases and capabilities, collaborate with internal and external AI communities...

Secure enterprise AI-enabled document generative AI capabilities.

Secure enterprise AI-enabled question and answer ChatBot capabilities for natural language querying of documents and data.

The AVS International office is required to identify means of compliance to ICAO Standards and Recommended Practices (SARPs). Both SARPs and means of compliance evidence are text paragraphs scattered across thousands...

AVS identified a need to derive the joint aircraft system codes (JASC) chapter codes from the narrative description within service difficulty reports (SDR), a form of safety event reporting from aircraft operators.

The Multiple Airport Route Separation (MARS) program is developing a safety case for reduced separation standards between Performance Based Navigation (PBN) routes in terminal airspace.

Successfully demonstrated use of an AI capability to analyze camera images of a wind sock to produce highly accurate surface wind speed and direction information in remote areas that don’t have a weather observing s...

ROMIO is an operational demonstration to evaluate the feasibility to uplink convective weather information to aircraft operating over the ocean and remote regions.

Description: Utilize deep learning models for predicting head kinematics directly from crash videos.

Description: Utilize deep learning for predicting crash parameters, Delta-V (change in velocity) and PDOF (principal direction of force), directly from real-world crash images.

Description: Deep learning computer vision algorithms aimed at analyzing aggregate particle size grading.

Description: DeepCNet-based neural network to identify and classify track-related features (e.g., track components, such as fasteners and ties) for ""change detection"" applications.

Description: Leveraging large volumes of these recursive track geometry measurements to develop and implement automated machine-learning-based processes for analyzing, predicting, and reporting track locations of conc...

Description: The ongoing work leverages AI/ML in big traffic data analytics to identify roadway geolocations where potential safety issue may exist through the integration of connected vehicle data, speed and Individu...

Description: A data science lab established at Turner-Fairbank Highway Research Center.

Description: The Exploratory Advanced Research (EAR) Program addresses the need for longer term, higher risk research with the potential for transformative improvements to transportation systems.

DOT-Private Sector collaboration to develop advanced AI data structure expertise, lessons learned, and best practices to accelerate AI development across the agency.

The AICA Working Group consists of Operating Administration (OA) and Secretarial Offices AI leaders and subject matter experts across the Department.

The business goal of this project is to use a powerful language model to analyze public comments.

DOT needs a large language model that can integrate with word processing, email, presentations and shared drives.

DOT employees need an environment to upload custum Retrieval-Augmented Generation (RAG).

CPC Outlook Generator (COG) is a front end Graphical User Interface built to interact with a data model called CPC Onboarding Success Evaluator (COSE), a machine learning model capable of predicting how many Certified...

AI-based voice recognition of Air Traffic Control (ATC) and pilot communications associated with a flight is required for enhanced and accurate delay reporting and attribution.

Before digitization: * The Certification Services Oversight Process (CSOP) is a manually intensive paper process * Requires the public and small businesses to provide input for Certification requests * No external app...

Assisting the public in finding information on the FAA website.

"Utilize equipment telemetry data, statistical modeling and Machine Learning to predict equipment failures before they occur.

The use of AI modeling can categorize the types of events based on the information provided in the accident and incident report and its result will support the safety metric calculation.

To classify surface Mandatory Occurrence Reports (MORs) to reduce the amount of manual event review.

To transcribe recorded radio communications between pilots and air traffic controllers operating within the national airspace.

Unmanned Aircraft Systems (UAS) are a source of potential safety risk.

This use case will be used to enable new methods and tools that support the maintenance of life cycle of National Airpsace System (NAS) infrastructure.

The Copilot would be natively embedded across an application to deliver a consistent user experience that can answer questions, generate content, and dynamically automate any action.