2304352
Project Grant
Overview
Grant Description
SBIR Phase I: Scientific Discovery Translation of Snow-Covered Road Perception Software to a Lane Detection in Snow (LDIS) product - The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is improved automotive transportation safety, usability, and equity for the general public which reduces the annual 5,300 fatalities, 418,000 injuries, and billion-dollar losses from inclement weather crashes in the United States.
The technology identifies the driving lane using camera data processing in challenging driving conditions such as congested intersections and bridges, dark tunnels, and during sun glare and active snowfall. Addressing these problems also enables U.S. technology competitiveness in the global automotive market, development of technologies relevant to national defense and energy efficiency applications, expansions of existing university courses, and entrepreneurial engagement from underrepresented communities.
The foundation for the proposed research is the utilization of camera and global positioning data specifically for navigation in snow using real-time machine learning methods without an overreliance on deep learning. This technology can be implemented in current vehicles, enabling a widespread commercial impact and a strong means to grow a viable business that is generating tax revenue and offering technology jobs to the local community.
The strong technical innovation of this work is a hierarchical computer vision system built using a resilience engineering methodology, individually tuned classifications, camera and GPS fusion, and fast processing machine learning. This system provides verification of successful performance with respect to human-observed ground truth without an overreliance on deep learning so that it can be successfully validated by automotive companies using standard practices.
This innovation allows current driving assistance products to remain functional when they are needed most: in low visibility, low traction situations. This research aims to verify the innovation in two-lane intersections, bridges, tunnels, under sun glare conditions, in 100+ miles of active snowfall, and in instances of misleading environmental information. Data for these instances will be collected and the existing technology will be modified and improved.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
The technology identifies the driving lane using camera data processing in challenging driving conditions such as congested intersections and bridges, dark tunnels, and during sun glare and active snowfall. Addressing these problems also enables U.S. technology competitiveness in the global automotive market, development of technologies relevant to national defense and energy efficiency applications, expansions of existing university courses, and entrepreneurial engagement from underrepresented communities.
The foundation for the proposed research is the utilization of camera and global positioning data specifically for navigation in snow using real-time machine learning methods without an overreliance on deep learning. This technology can be implemented in current vehicles, enabling a widespread commercial impact and a strong means to grow a viable business that is generating tax revenue and offering technology jobs to the local community.
The strong technical innovation of this work is a hierarchical computer vision system built using a resilience engineering methodology, individually tuned classifications, camera and GPS fusion, and fast processing machine learning. This system provides verification of successful performance with respect to human-observed ground truth without an overreliance on deep learning so that it can be successfully validated by automotive companies using standard practices.
This innovation allows current driving assistance products to remain functional when they are needed most: in low visibility, low traction situations. This research aims to verify the innovation in two-lane intersections, bridges, tunnels, under sun glare conditions, in 100+ miles of active snowfall, and in instances of misleading environmental information. Data for these instances will be collected and the existing technology will be modified and improved.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Kalamazoo,
Michigan
49001-3657
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the total obligations have increased 6% from $275,000 to $292,875.
Revision Autonomy was awarded
Project Grant 2304352
worth $292,875
from National Science Foundation in August 2023 with work to be completed primarily in Kalamazoo Michigan United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Scientific Discovery Translation of Snow-Covered Road Perception Software to a Lane Detection in Snow (LDIS) Product
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is improved automotive transportation safety, usability, and equity for the general public which reduces the annual 5,300 fatalities, 418,000 injuries, and billion-dollar losses from inclement weather crashes in the United States. The technology identifies the driving lane using camera data processing in challenging driving conditions such as congested intersections and bridges, dark tunnels, and during sun glare and active snowfall. Addressing these problems also enables U.S. technology competitiveness in the global automotive market, development of technologies relevant to national defense and energy efficiency applications, expansions of existing university courses, and entrepreneurial engagement from underrepresented communities. The foundation for the proposed research is the utilization of camera and global positioning data specifically for navigation in snow using real-time machine learning methods without an overreliance on deep learning. This technology can be implemented in current vehicles, enabling a widespread commercial impact and a strong means to grow a viable business that is generating tax revenue and offering technology jobs to the local community._x000D_ _x000D_ The strong technical innovation of this work is a hierarchical computer vision system built using a resilience engineering methodology, individually tuned classifications, camera and GPS fusion, and fast processing machine learning. This system provides verification of successful performance with respect to human-observed ground truth without an overreliance on deep learning so that it can be successfully validated by automotive companies using standard practices. This innovation allows current driving assistance products to remain functional when they are needed most: in low visibility, low traction situations. This research aims to verify the innovation in two-lane intersections, bridges, tunnels, under sun glare conditions, in 100+ miles of active snowfall, and in instances of misleading environmental information. Data for these instances will be collected and the existing technology will be modified and improved._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
MO
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 5/6/24
Period of Performance
8/1/23
Start Date
7/31/24
End Date
Funding Split
$292.9K
Federal Obligation
$0.0
Non-Federal Obligation
$292.9K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2304352
Additional Detail
Award ID FAIN
2304352
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
X5QYCJSZGJU8
Awardee CAGE
9CHC7
Performance District
MI-04
Senators
Debbie Stabenow
Gary Peters
Gary Peters
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $275,000 | 100% |
Modified: 5/6/24