Amendment 2 - NOFO No. 693JJ323NF00008 - EARP.pdf
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- Exploratory Advanced Research Program
- Opportunity number
- 693JJ324NF00008
About this file
This document is an amendment to a Notice of Funding Opportunity (NOFO) from the U.S. Department of Transportation Federal Highway Administration (FHWA) for the Exploratory Advanced Research (EAR) Program. The amendment provides questions and answers from a webinar held on May 8, 2024 related to the NOFO.
The NOFO seeks applications for cooperative agreements to collect diverse pedestrian data and provide researchers access to this data to encourage novel approaches for analysis in support of increasing the safety and mobility of all people. FHWA anticipates making awards totaling around $2,000,000, with individual awards up to $250,000 in federal funding, requiring a 20% non-federal match. The NOFO includes two tracks: Track A for collecting naturalistic pedestrian data, and Track B for data management and distribution. Proposals may be submitted for one or both tracks. FHWA is seeking diversity of locations and road types for the data collection. The authority and funding for this effort come from 23 U.S.C. 502(b)(3)(C) and the Bipartisan Infrastructure Law (P.L. 117-58) Sec. 11101(c)(1)(A), respectively.
Amendment 2 to NOFO Webinar Q&As
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| File | Type | Posted |
|---|---|---|
| Amendment 1 - NOFO No. 693JJ324NF00008 - EARP.pdf | ||
| 693JJ324NF00008.pdf |
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U.S. Department of Transportation
Notice of Funding Opportunity Number 693JJ324NF00008
“Exploratory Advanced Research (EAR) Program”
Amendment #2
Dated 05/20/2024
This amendment to NOFO 693JJ324NF00008 is to post the Questions & Answers for the
Notice of Funding Opportunity (NOFO) webinar from May 8, 2024:
NOFO 693JJ323NF00008 May 8, 2024, Webinar Question and Answers
Except for the statutes and regulations cited, the contents of this document do not have the force and effort of law and are not meant to bind the public in any way. This document is intended only to provide information regarding existing requirements under the law or agency policies.
1. Question: Are you interested in GPS tracks from pedestrians? For boosting the privacy and amount of GPS tracks, would you be interested in realistic, simulated GPS tracks from pedestrians for any location in the U.S?
Answer: GPS data is within scope. The intent of Track A research is to provide naturalistic data.
2. Question: Could data modalities such as eye tracking or heart rate be included?
Answer: Yes, data modalities may include physiological data given the intent is to collect naturalistic behavior.
3. Question: Are entities outside the U.S. eligible for awards?
Answer: Yes
4. Question: What volume of data is FHWA expecting?
Answer: The volume should reflect the size of the awards. Data diversity is more important than volume.
5. Question: What is the total funding for the program that will be used for awards?
Answer: The NOFO indicates that FHWA anticipates making awards totaling around
$2,000,000.
6. Question: Question: What is the maximum federal budget we can apply for? $250,000 or
$200,000? Is the Federal max per award is $250,000 .Is the 20 percent match of the project total (FHWA plus Cost share) or the FHWA amount ($50,000)?
Answer: The notice is for awards up to $250,000 in federal funding. Federal funding of
$250,000 would require match funding of $50,000 (20 percent) or more so a total budget of $300,000.
7. Question: Would Track A and B be awarded at the same time? Is the Period of performance for both the same?
Answer: FHWA intends to make awards for both tracks in fiscal year 2024. It is possible that the period of performance of Track B projects would reflect timing of data collections that will not be complete by the start of the award.
8. Question: Do you have a specific requirement for the modality of data that will be distributed? Say, if the original modality is camera, do you expect the videos to be shared, or you encourage data that derived information from the videos and can be shared. (As part of Track A).
Answer: The intent is for other researchers to have access to useful pedestrian behavior data. As part of the balance between privacy and data access, there may be broader access to derived data than the originally collected video.
9. Question: The data collection is only for pedestrian or other micro-mobility users.
Answer: For this request, pedestrians. Pedestrian data may include pedestrians with mobility limitations that require use of assistive devices including motorized wheelchairs of scooters.
10. Question: Could you clarify partnership with the people who will be part of the collection? What sort of entities do you mean for example?
Answer: The idea would be to work with stakeholders that represent targeted groups of pedestrians.
11. Question: Could you talk a bit more about the "partnership aspect" of the merit review process? Do we need to show partnerships with potential consumers of the data (e.g., researchers who may end up using the collected data) or partnership with the target population and the city/county/state where data collection will happen?
Answer: For Track A FHWA is seeking researchers to partner with groups that represent targeted pedestrian populations. For Track B, partnerships may include data consumers.
12. Question: Can one proposer go after the two tracks (Track A and Track B) together?
Answer: Yes. One person may submit a proposal in Track A and a separate proposal in
Track B. FHWA will be considering the proposals separately so a proposal in Tack B should not depend on award of a proposal in Track A.
13. Question: With the limited amount of funding for each proposal, are you weighting more on uniqueness or geographic diversity?
Answer: FHWA is seeking diversity of locations and road types.
14. Question: Is the FHWA scan study available to proposers?
Answer: The FHWA scanning activities are not in a published document. Following are references that FHWA considered in scoping the notice.
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2009). Caltech Pedestrians [dataset].
https://data.caltech.edu/records/f6rph-90m20
Is this the end of traffic jams? (n.d.). Retrieved October 26, 2023, from https://www.bbc.com/future/article/20181212-can-artificial-intelligence-end-traffic-jams
Jabłoński, P., Iwaniec, J., & Zabierowski, W. (2022). Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets.
Sensors (Basel, Switzerland), 22(18), 7014. https://doi.org/10.3390/s22187014
Katapult Memories: A Study on LiDAR (2018). (n.d.). Retrieved November 8, 2023, from https://www.katapultengineering.com/blog/pros-and-cons-of-lidar
Lin, P.-S., Kourtellis, A., Wang, Z., Chen, C., & University of South Florida. Center for
Urban Transportation Research. (2019). Integration of a Robust Automated
Pedestrian Detection System for Signalized Intersections.
https://rosap.ntl.bts.gov/view/dot/64442 https://data.caltech.edu/records/f6rph-90m20 https://www.bbc.com/future/article/20181212-can-artificial-intelligence-end-traffic-jams https://www.bbc.com/future/article/20181212-can-artificial-intelligence-end-traffic-jams https://doi.org/10.3390/s22187014 https://www.katapultengineering.com/blog/pros-and-cons-of-lidar https://rosap.ntl.bts.gov/view/dot/64442
Muthukumar, V., Morris, B., Regentova, E., Paz, A., Breen, E., Shirazi, M., Shahraki, F.
F., Lan, M., Parker, M., & University of Nevada, L. Vegas. T. R. C. (2015).
Automated Pedestrian Detection, Count and Analysis System (527-14–803).
https://rosap.ntl.bts.gov/view/dot/35379
Pedestrian and bicyclist detection with thermal imaging cameras | Teledyne FLIR. (n.d.).
Retrieved October 26, 2023, from https://www.flir.com/discover/traffic/urban/pedestrian-and-bicyclist-detection-with-thermal-imaging-cameras/
Pedestrian Traffic Fatalities by State: 2020 Preliminary Data | GHSA. (n.d.). Retrieved
November 8, 2023, from https://www.ghsa.org/resources/Pedestrians21
Smart Road Infrastructure eBook from Intel. (n.d.). Intel. Retrieved October 26, 2023, from https://www.intel.com/content/www/us/en/transportation/resources/road-infrastructure-ebook.html
Smith, S. F., Barlow, G. J., Hu, H.-C., Hua, J.-H., & Carnegie-Mellon University.
Technologies for Safe and Efficient Transportation University Transportation
Center. (2016). Pedestrian friendly traffic signal control: Final research report.
https://rosap.ntl.bts.gov/view/dot/31228
Tian, D., Han, Y., Wang, B., Guan, T., & Wei, W. (2021). A Review of Intelligent
Driving Pedestrian Detection Based on Deep Learning. Computational
Intelligence and Neuroscience, 2021, e5410049.
https://doi.org/10.1155/2021/5410049
What is lidar? Learn How Lidar Works. (n.d.). Velodyne Lidar. Retrieved November 8, 2023, from https://velodynelidar.com/what-is-lidar/
Zhang, S., Benenson, R., & Schiele, B. (2017). CityPersons: A Diverse Dataset for
Pedestrian Detection. 2017 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 4457–4465. https://doi.org/10.1109/CVPR.2017.474
-- END OF AMENDMENT 2 --
https://rosap.ntl.bts.gov/view/dot/35379 https://www.flir.com/discover/traffic/urban/pedestrian-and-bicyclist-detection-with-thermal-imaging-cameras/ https://www.flir.com/discover/traffic/urban/pedestrian-and-bicyclist-detection-with-thermal-imaging-cameras/ https://www.ghsa.org/resources/Pedestrians21 https://www.intel.com/content/www/us/en/transportation/resources/road-infrastructure-ebook.html https://www.intel.com/content/www/us/en/transportation/resources/road-infrastructure-ebook.html https://rosap.ntl.bts.gov/view/dot/31228 https://doi.org/10.1155/2021/5410049 https://velodynelidar.com/what-is-lidar/ https://doi.org/10.1109/CVPR.2017.474
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