SOLICITATION NOTICE
A -- FHWA 2022 Exploratory Advanced Research Broad Agency Announcement - 693JJ3-22-BAA-0001
- Notice Date
- 10/13/2022 9:40:55 AM
- Notice Type
- Combined Synopsis/Solicitation
- NAICS
- 541715
— Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Contracting Office
- 693JJ3 ACQUISITION AND GRANTS MGT WASHINGTON DC 20590 USA
- ZIP Code
- 20590
- Solicitation Number
- 693JJ322BAA0001
- Response Due
- 12/5/2022 11:00:00 AM
- Archive Date
- 12/20/2022
- Point of Contact
- Freida L. Byrd, Phone: 2023666547, Fax: 2023663705, Robin K. Hobbs, Phone: 2023664004, Fax: 2023663705
- E-Mail Address
-
freida.byrd@dot.gov, Robin.Hobbs@dot.gov
(freida.byrd@dot.gov, Robin.Hobbs@dot.gov)
- Description
- This synopsis is issued in accordance with FAR Part 5.201 in response to the mandatory requirement for a pre-solicitation notification. The purpose of this synopsis is to announce the Federal Highway Administration�s intent to issue a Broad Agency Announcement (BAA) for the Exploratory Advanced Research (EAR) Program. As a result of this BAA, the FHWA intends to award contracts and/or cooperative agreements. Offerors shall propose periods of performance based on individual proposed research efforts in accordance with the guidance in the BAA.� Important dates anticipated for the BAA are as follows: BAA Opens in https://sam.gov/� � � � � � � � � �October 13, 2022 Question Period Closes��������������������������������� November 4, 2022 Proposals Due 2:00 pm EST ������������������������ December 5, 2022 Anticipated Award Date:� ��������������������������� February 2023 The Federal Highway Administration (FHWA) is soliciting for proposals under its EAR Program for research projects that could lead to transformational changes and truly revolutionary advances in highway engineering and intermodal surface transportation in the United States. This program supports scientific investigations and studies that advance the current knowledge and state-of-the-art in the sciences and technologies employed in the planning, design, construction, operation, maintenance and management of the nation�s highways. Strategically, this research will enable and expedite the development of revolutionary approaches, methodologies, and breakthroughs required to drive innovation and greatly improve the efficiency of highway transportation. The FHWA anticipates sponsoring research addressing the following two topics through the issuance of this BAA including the following: Topic 1: Artificial Intelligence for Highway Transportation In the last decade, there has been an exponential increase in artificial intelligence (AI) research and development building on access to massive amounts of new data, improved computer processing and data storage, and new communications bandwidth.� Broadly AI applications fall into two areas, one relating to robotics where tools such as machine learning and computer vision are integrated into real time, active control systems, for example in vehicle automation, and a second area where AI applications are solely computational.� For highway transportation, much of the private investment and public attention has been on the area relating to vehicle control and automation.� Government funding in contrast has focused on system-level benefits � often solely computational � that could substantially increase public safety and mobility.� With the growing number of maturing and commercial applications, there still is a need for early state research to support emerging advances in AI than can solve even more complex questions in highway transportation.� Accordingly, the FHWA EAR Program now is seeking to demonstrate the potential of untried advances in AI for solving nationally critical questions in highway transportation.� Examples that FHWA found where AI applications have the potential to solve critical highway transportation questions include: Safety and Mobility Vulnerable road users � Use of AI to analyze data or develop solutions focusing on vulnerable road user safety, especially for understudied topics such as safety outside urban settings or in traditionally underserved communities. Pedestrian, cyclist, and micro-mobility detection � Use of machine vision and other AI techniques to analyze pedestrians, cyclists, and micro-mobility device movement on roadways and in intersections to improve signal performance for these modes for all travelers including people using assistive devices such as wheelchairs.� Pedestrian wayfinding � Use of AI to highly automate dynamic, mapping of pedestrian environment including sidewalks, crosswalks, pathways, transit centers, and other public and private locations. Bridge strikes � Use of AI including video analytics to increase understand of root causes for why vehicles, in particular large vehicles, come into conflict with bridges and other highway structures. Smart truck parking � Use of AI including video analytics to detect and predict of the availability of truck parking spaces at rest stops with high accuracy. Highly accurate prediction of truck parking availability would improve safety by making it easier for drivers to locate parking. Infrastructure Modernization Physics guided AI-based solutions for predictive asset performance � In general AI systems are based on statistical inference so may provide results that defy realistic physics-based constrains.� Integration of physics into AI may help to bridge the gap between collecting data and using it effectively for decision making.� Note: Proposals/Applications should discuss how such approaches compare to current predictive modeling approaches used by asset owners.� Interpreting sensor data � Asset owners struggle to analyze all the data that comes out of their infrastructure condition sensors. This topic could explore ways to use AI increase the reliability and automation of translating raw sensor data to actionable information for asset owners.� Note: proposals/applications on this topic need to articulate how they are different from and substantially better than current work and how results could integrate into current inspection or asset management systems.� Cross cutting Significantly increase the ability to process data or integrate disparate data by reducing or eliminating the need for manual data pre-processing or for experts to interpret complex data for highway transportation.� Incorporate edge computing with roadside hardware to improve data security and privacy, increase the speed of analysis, and reduce resources needed for moving, storing, and analyzing data for highway transportation. FHWA identified the above topics through a scanning process.� There may be other topics that exploratory advanced research can demonstrate the potential for AI for solving issues of national importance.� Offerors that want to suggest other topics should make clear why the topic is exploratory advanced research, of national importance, and would be unlikely to advance without EAR Program funding.� Note: FHWA anticipates that proposals/applications should Provide a clear description of data proposed for training and testing including issues such as data rights, security, privacy, diversity, and bias; and Include a team composed of experts and partners from different disciplines and sectors for transition of results. Topic 2: New Approaches to Reduce Embodied Carbon from Infrastructure Construction, Maintenance, and Operations Recent fundamental advances in science and technology can enhance existing or provide new data driven approaches for effectively assessing the impact of highway infrastructure on the earth�s climate and engineer construction materials that provide better engineering as well as environmental performance than those currently used.� Examples include but are not limited to sensing and communications that allow for ubiquitous tracking of materials from extraction through re-use, machine learning approaches that are explainable, contextually valid, and transferable, and multi-scale modeling that are flexible and open. There are two key thrusts under this topic. The first thrust is the ability to quantify and predict impacts of embodied carbon from infrastructure considering the full lifecycle and at multiple scales from material choice at a project level through system-level planning.� This thrust should note key decision points where environmental impact assessments can be incorporated into decision-making to effect change, where data are lacking and address approaches for addressing these gaps through novel data sources or analytics techniques. This thrust may include new methods for obtaining and analyzing data relevant to quantifying climate impacts from highway infrastructure across scales from project to system levels.� New methods and approaches may include the use of mobile, vehicle-based sensors.� This thrust also may consider new methods for extrapolating across data gaps where there may be ahistorical changes in trends or frequency of rare events.� The data gathered through these efforts can then be coupled with the lifecycle assessment (LCA) methodology to quantify embedded carbon and other environmental or climate impacts. This thrust also may include new methods for modeling that can predict performance reflecting dramatic changes in trends or frequency of rare events.� Current models lack flexibility to account for dynamic environmental changes or ability to adjust to regional or local differences.� Accordingly, FHWA also is seeking under this thrust modeling approaches that can provide multi-scale and explainable information to support key decision points from planning, development, design, construction, and operations that can accounts for multiple objectives including safety, performance, cost, and climate impacts.� This thrust should take into account vehicle technology advances and dynamic non-historical changes in the environment that could take place during highway infrastructure lifecycles.� Methods should be capable of considering societal costs including cost of closures and should provide flexibility to reflect regional and local differences.� Proposals/Applications should describe access to or collection of ground truth data for training or testing of algorithms or models. Research should describe how models may interface with existing systems or practices, for example, how designers may use vehicle-pavement interaction models or how system owners and operators use asset management systems. The second thrust is the use of primary materials for highway infrastructure � cement, asphalt, aggregates, or steel (reinforcement) -- that can provide lower levels of embedded carbon from extraction through production, transportation, construction, lifecycle, and potential re-use. The FHWA EAR Program has funded relevant materials research on supplementary and alternative cementitious materials.� More information is located at: Index - Supplementary Cementitious Material Advancements: Helping to Make Longer Lasting Concrete Highways and Transportation Structures , August 2020 - FHWA-HRT-20-048 (dot.gov), Index - Alternative Cementitious Materials in Transportation , August 2018 - FHWA-HRT-18-031 (dot.gov), Index - Inorganic Polymers: Novel Ordinary Portland Cement-Free Binders for Transportation Infrastructure , April 2019 - FHWA-HRT-18-029 (dot.gov), Index - Mechanisms of Hydration and Setting of Ordinary Portland Cement in Simple and Complex Systems , April 2019 - FHWA-HRT-17-102 (dot.gov), and Novel Alternative Cementitious Materials for Development of the Next Generation of Sustainable Transportation Infrastructure , October 2015 - FHWA-HRT-16-017 (dot.gov) The program also has funded materials research on supplements to asphalt binders.� More information is located at: Index - Improving the Compatibility of Waste , August 2021 - FHWA-HRT-21-084 (dot.gov), and Index - Researching Novel Approaches for Aging Resistant Binder Technologies ,August 2020 - FHWA-HRT-20-051 (dot.gov) At this time, FHWA is not seeking new research that is similar to or building on recent and ongoing funded projects.� FHWA is interested in other types of low carbon materials or approaches that could reduce greenhouse gas (GHG) emissions across the highway infrastructure lifecycle including transport, construction, and re-use.� Note: FHWA anticipates proposals/applications shall: Address one or the other thrust in Topic 2 � either the ability to quantify and predict impacts of embodied carbon from infrastructure considering the full lifecycle and at multiple scales or use of primary materials for highway infrastructure that can provide lower levels of embedded carbon from extraction through production, transportation, construction, lifecycle, and potential re-use � and not attempt to cover both. Include cross-disciplinary teams with experience in cutting edge research in computer science, civil engineering, material science as well as economics and other social sciences. Note:� Future Topic Areas FHWA continues to investigate other research areas for potential breakthrough opportunities.� FHWA welcomes questions or thoughts about opportunities and other areas of focus that could lead to transformation changes in highway research.�� At this time, however, FHWA has not identified other focus areas or topics for funding under the EAR Program.� For further information about the EAR Program, please see http://www.fhwa.dot.gov/advancedresearch/contacts.cfm.� The BAA will be released electronically via this new Government Point of Entry (GPE) known as SAM.gov located at https://sam.gov/.� As such, no written, telephonic or other type of request for an advance copy of the announcement will be entertained. Additional information along with instructions for submitting proposals/applications in response to the solicitation will be provided in the BAA. Potential offerors/vendors are encouraged to register on https://sam.gov/ to receive any further information in reference to the subject action inclusive of any announcements, and/or amendments to the solicitation after its release. Any questions regarding this synopsis should be directed to the Contract Specialist, Freida Byrd at Freida.Byrd@dot.gov or Contracting Officer, Robin Hobbs at Robin.Hobbs@dot.gov.
- Web Link
-
SAM.gov Permalink
(https://sam.gov/opp/8585fa573094445fbda99e76ec80182c/view)
- Record
- SN06492604-F 20221015/221013230100 (samdaily.us)
- Source
-
SAM.gov Link to This Notice
(may not be valid after Archive Date)
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