USA Banner

Official US Government Icon

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure Site Icon

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

U.S. Department of Transportation U.S. Department of Transportation Icon United States Department of Transportation United States Department of Transportation

Railroad Artificial Intelligence Intruder Learning System (RAIILS)

Document Series
Technical Reports
Author
Erick Vega, Pasi Lautala, David Nelson, Charlotte Weinstein, Colin Brooks, Richard Dobson
Report Number
DOT/FRA/ORD-24/38
Office
RDI-23
Subject Research, Communications, Train Control
Keywords
Artificial intelligence, AI, machine learning, railroad, crossing, security, UAV, UAS, drone, automated
Document
RAiILS Report.pdf (2.41 MB)

This report summarizes the research conducted as part of Phase 2 of the Railroad Artificial Intelligence Intruder Learning System (RAIILS), led by a team of researchers at Michigan Technological University (Michigan Tech) and sponsored by the Federal Railroad Administration (FRA) from March 2019 to September 2021. The project had two primary objectives: (1) to help FRA understand how Artificial Intelligence (AI) models could be applied to railroad trespass detection, and (2) to assist North American railroads in developing machine learning capabilities to detect railroad trespassers in real time. The Phase 1 report served primarily as a literature review and focused on addressing the first of these goals.


DOT is committed to ensuring that information is available in appropriate alternative formats to meet the requirements of persons who have a disability. If you require an alternative version of files provided on this page, please contact FRADevOps@dot.gov.
Last updated: Thursday, September 19, 2024