| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | April 30, 2019 |
| Latest Amendment Date: | November 10, 2023 |
| Award Number: | 1922782 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Vishal Sharma
vsharma@nsf.gov (703)292-0000 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | May 1, 2019 |
| End Date: | April 30, 2024 (Estimated) |
| Total Intended Award Amount: | $1,999,770.00 |
| Total Awarded Amount to Date: | $1,999,770.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1523 UNION RD RM 207 GAINESVILLE FL US 32611-1941 (352)392-3516 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
FL US 32611-6120 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | S&CC: Smart & Connected Commun |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The goal of this project is to further the ability of cities and communities to deploy technology that saves lives through safer transportation systems. The approach is to create open source analytics solutions to enable novel transportation applications that utilize data from low-cost video sensors. Video data are processed using edge computing (inexpensive computing hardware that performs analysis without storing significant amounts of data) in order to reduce the amount of data stored. Social dimensions of the research project emerge from the deep research partnership between the City and the University, with the goal to provide replicable and near-term social impacts. The project aligns with the Vision Zero concept to reduce traffic fatalities, with programs that are based on education, enforcement and design. By understanding the risk profile of an intersection through automated detection of near miss events, communities will be able to proactively design and alter streets and intersections to be safer.
The goal of designing a smart city, when addressing the technical challenges at the intersection, street and system levels, has several research components. (i) Development of new algorithms for multi-target tracking: The problems of occlusion, temporal assignment of features to objects and target motion will be jointly formulated. (ii) Integrated optimization and simulation for signal control: We formulate the problem of estimating signal control parameters (offsets, phasing etc.) in a network as one of global optimization. (iii) Real-time reinforcement learning is a natural choice when online machine learning meets real world feedback from the City. Our ability to obtain and analyze continuous-time data at the network level will provide insights on how conflict points and patterns can change through the network. This is expected to impact decisions in traffic management, smart city planning and safety.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
We have created a sophisticated system that combines data from cameras, LiDAR, and traffic sensors to monitor and analyze traffic in real-time. Our technology focuses on identifying individual vehicles and pedestrians, capturing their positions and speeds. The use of LiDAR is particularly useful during nighttime when cameras may not work as effectively. All the gathered data is mapped onto a Google Maps-based coordinate system, which allows us to track movement patterns and identify potential collision points between vehicles and pedestrians, aiding in recognizing both near-misses and serious incidents.
The video tracking component relies on a machine learning model known as YOLO to detect various road users such as vehicles, pedestrians, cyclists, and motorcyclists. Alongside this, we utilize state-of-the-art algorithms to process LiDAR data, creating three-dimensional models of detected objects, which helps in understanding their size and movement direction. Our system also employs Kalman filters to predict future movements based on current data, and these predictions are crucial in determining possible future collisions. This information is displayed as video clips and heatmaps, pointing out conflict zones around intersections, particularly using ground sensor data that offers detailed insights into traffic flows.
Our system features high-frequency data from ground sensors at numerous intersections, which helps in predicting traffic movements and suggesting improvements to signal timings to enhance safety. We analyze this data alongside video and LiDAR inputs to compute severe events that are extensions of traditional measures based on Time to Collision (TTC) and Post Encroachment Time (PET). These severe events estimate the risk levels of potential collisions. By adhering to thresholds for these metrics, any detected severe events signify intersections where interventions might be beneficial. This comprehensive examination of traffic interactions allows for better understanding and prevention of accidents, helping traffic management authorities plan effective countermeasures.
Last Modified: 08/30/2024
Modified by: Sanjay Ranka
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