SPECIAL NOTICE
99 -- Intent to Sole Source
- Notice Date
- 8/27/2020 11:46:01 AM
- Notice Type
- Special Notice
- NAICS
- 541715
— Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Contracting Office
- FEDERAL RAILROAD ADMINISTRATION
- ZIP Code
- 00000
- Solicitation Number
- FR20RPD31000000046
- Response Due
- 8/27/2020 12:00:00 PM
- Archive Date
- 09/11/2020
- Point of Contact
- Moyah Wilson
- E-Mail Address
-
moyah.wilson@dot.gov
(moyah.wilson@dot.gov)
- Description
- Use of Laser Triangulation and Deep Neural Networks (DNNs) for Railway Safety Inspections Phase 2, Synopses (Sole Source Intent Notice) The Federal Railroad Administration (FRA), Washington, DC, intends to award a Sole Source, Cost-No Fee Contract to The University of Illinois for a a continuation of research begun under FRA Contract 693JJ619C000004 and is intended to develop and demonstrate the application of artificial intelligence to laser-based range and intensity data for railroad track safety inspection.� This development effort is a unique combination of 3-dimensional laser data coupled with deep neural networks (DNN) to detect and report changes in railroad track that may impact operational safety and is a logical extension of successful research conducted by this team in the Phase 1 project.� This Phase 2 effort is inexorably linked to the prior work, and will leverage the products produced during the Phase 1 effort to establish the relationships between track change data and traditional inspection methods, i.e. track geometry, to produce products for rail inspectors and maintainers that are directly consumable.� This new work will also establish clear metrics and reports to aid decision makers during resource prioritization activities.��� Automated track change detection technologies are the next generation of inspection tools to ensure track safety.� This project combines the unique talents and experience of a proven team and systems to create systems and products that will effectively transform the track inspection activity. This work is directly supported by rail industry partners to help ensure project success.� The results of this development effort will be widely disseminated through the rail industry, via FRA publications, industry conference reports and presentations. . This synopsis is not a request for competitive proposals; however interested parties may identify their interest and capability to respond to the requirement. Respondents must provide a capability statement for a �Use of Laser Triangulation and Deep Neural Networks (DNNs) for Railway Safety Inspections Phase 2�. �Oral communications are not acceptable in response to this synopsis. Interested parties with the capability of meeting the proposed requirements are requested to provide an E-mail to the FRA point of contact, Ms. Moyah Wilson, (moyah.wilson@dot.gov) no later than 3:00 p.m. EST Thursday, September 10, 2020. Such capabilities/qualifications will be evaluated solely for the purpose of determining whether or not to conduct this procurement on a competitive basis. A determination by the Government not to compete this proposed effort on a full and open competition basis, based upon responses to this notice, is solely within the discretion of the Government.
- Web Link
-
SAM.gov Permalink
(https://beta.sam.gov/opp/ae1b569b559344e9abd64959e256d10c/view)
- Place of Performance
- Address: USA
- Country: USA
- Country: USA
- Record
- SN05775624-F 20200829/200827230204 (samdaily.us)
- Source
-
SAM.gov Link to This Notice
(may not be valid after Archive Date)
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