| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
|
| Initial Amendment Date: | March 22, 2019 |
| Latest Amendment Date: | March 22, 2019 |
| Award Number: | 1849246 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | April 1, 2019 |
| End Date: | March 31, 2023 (Estimated) |
| Total Intended Award Amount: | $179,999.00 |
| Total Awarded Amount to Date: | $179,999.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
438 WHITNEY RD EXTENSION UNIT 1133 STORRS CT US 06269-9018 (860)486-3622 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
371 Fairfield Way, Unit 4155 Storrs CT US 06269-4155 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | S&AS - Smart & Autonomous Syst |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
In smart cities of the future, how to sense, understand, and manage urban-scale vehicular systems, e.g., taxis, in an autonomous fashion (with little or no human intervention) is an essential topic to improve urban mobility efficiency, such as shorter waiting time for passengers, lower cruising mileage for drivers, and higher revenues for vehicular system operators. However, the current management strategies for vehicular systems are mainly based on individual-level data knowledge, ignoring rich information from a fleet perspective. In this project, the investigators design and implement a fleet-oriented management strategy for vehicular systems, which utilizes real-time data from sensors installed in all vehicles to improve the overall performance of the vehicular system. In particular, the investigators aim to improve the taxi system performance by using onboard cameras to detect waiting passengers on streets and share this information with nearby vehicles and dispatch centers to pick up these passengers through a dispatching strategy. The research team will develop a clear understanding of how to design an adaptive autonomous vehicular sensing and dispatching strategy to improve urban mobility efficiency from a fleet-oriented perspective, with potential applications to future fully autonomous fleets. Such an understanding on vehicular sensing and dispatch will improve the quality of the every-day life such as more efficient commute for passengers, and lower energy uses for drivers, and finally improve the environment for the society by low vehicle mileage.
This research develops a fleet-oriented sensing and control framework to enable seamlessly integration of historical and real-time data within a fleet for adaptive vehicular sensing, modeling, and control. Specifically, this project studies how to best use spatiotemporally-correlated contextual information (e.g., vehicular mobility, service demand, disruptive events) among vehicles. Although such correlations decay over time and distance, it is possible to enable autonomous vehicular sensing, modeling, and control adaptively based on the following research to develop novel services: (i) reconfigurable fleet-wide coordinated sensing by autonomously learning correlated vehicular interactions; (ii) models of mobility phenomena by collectively interpreting implicit data from different vehicles with a combination of deep learning, structured learning, and attribute-based learning; (iii) designs of robust dispatching strategies with uncertainty sets and receding horizon control frameworks by iteratively considering fleet-wide knowledge to improve mobility efficiency.
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
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
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.
The major goals of this project include repurposing urban fleets by exploring their dynamic mobility patterns, biased sensing coverage, and implicit data for fleet-oriented urban sensing and control in an autonomous fashion. To address this challenge, we have designed new solutions to seamlessly integrate fleet-wide knowledge (or potentially across different fleets) for reflective vehicular sensing and control. In particular, we have developed data-driven optimization theories and algorithms, multi-agent reinforcement learning methods for decision-making of autonomous mobility-on-demand systems and fleet of autonomous vehicles. In the past year, the findings and results of this project are published in top tier IEEE/ACM conferences and have been accepted and published to IEEE/ACM journals with high impacts. The main results contribute to the connected and autonomous vehicles system research society, by exploring the benefits of sharing information in terms of prediction, control performance and system efficiency.
The findings and results will also bridge the gap between data mining, mobile computing, ubiquitous computing, communication, control and learning society to develop well-integrated protocols for connected autonomous vehicles research.Two Graduate Research Assistants at Uconn were trained during this project, and one undergraduate capstone project and two high school scholar projects have been advised based on the major goals and research ideas from this project at UConn.The findings are also used as lecture materials for the "Introduction to Machine Learning" course for the Department of Computer Science and Engineering at the University of Connecticut and the Smart Cities at Rutgers CS department, which brought impacts to the education effort at both undergrad level and graduate level.
Last Modified: 08/13/2023
Modified by: Fei Miao
Please report errors in award information by writing to: awardsearch@nsf.gov.
