Railroad Artificial Intelligence Intruder Learning System (RAIILS)

Vega, Erick; Lautala, Pasi; Nelson, David N; Weinstein, Charlotte; Brooks, Colin N.; Dobson, Richard J.; Michigan Technological University

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Railroad Artificial Intelligence Intruder Learning System (RAIILS)

2024-09-19
By Vega, Erick ; Lautala, Pasi ; Nelson, David N ; ...

[PDF-2.41 MB]

English

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Details:

Creators:

Vega, Erick ; Lautala, Pasi ; Nelson, David N ; Weinstein, Charlotte ; Brooks, Colin N. ; ... More +

Vega, Erick ; Lautala, Pasi ; Nelson, David N ; Weinstein, Charlotte ; Brooks, Colin N. ; Dobson, Richard J. Less -
Corporate Creators:

Michigan Technological University
Corporate Contributors:

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development ; United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Less -
Subject/TRT Terms:

[+]

Artificial Intelligence Drones Machine Learning Railroad Grade Crossings Railroads Security Unmanned Aerial System [UAS] Unmanned Aerial Vehicle[UAV] Unmanned Aircraft Systems
Publication/ Report Number:

DOT/FRA/ORD-24/38
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

March 21, 2019–September 12, 2021
Corporate Publisher:

United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology
Abstract:

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.
Format:

PDF
Funding:

693JJ619C000001
Collection(s):

Federal Railroad Administration
Main Document Checksum:

[+]

urn:sha-512:388d81aedd4954de46f9793ce68b8f673c7ded36bbc8fc0521fbb58d2e7c4724c5d227577f102d3403d4a64e4c062a051784036cb6aa9c958a423f309125d3d4
Download URL:

https://rosap.ntl.bts.gov/view/dot/77626/dot_77626_DS1.pdf
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Railroad Artificial Intelligence Intruder Learning System (RAIILS)

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