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Project Information

CPS: TTP OPTION: MEDIUM: COLLABORATIVE RESEARCH: SMOOTHING TRAFFIC VIA ENERGY-EFFICIENT AUTONOMOUS DRIVING (STEAD)

Agency:
NSF

National Science Foundation

Project Number:
1837481
Contact PI / Project Leader:
PICCOLI, BENEDETTO
Awardee Organization:
RUTGERS THE STATE UNIVERSITY OF NEW JERSEY CAMDEN

Description

Abstract Text:
Studies show five of the top 10 most-gridlocked cities in the world are in the United States. Traffic congestion puts undue burden on transportation systems across the United States, raising transportation costs and the energy footprint. Vehicle automation creates an opportunity to reduce traffic and improve efficiency of the transportation infrastructure. In particular, this project aims to reduce the energy footprint of phantom traffic jams, where dense traffic comes to a halt for no apparent reason, and also stop-and-go-waves in congestion. The research team aims to reduce the overall energy footprint of stop-and-go congestion by up to 40% via a small portion of connected and autonomous vehicles (CAVs) inserted into normal traffic with drivers, also known as manned traffic. The work will build models of mixed autonomy (a combination of CAVs and manned traffic), and test the ability for this portion of CAVs to smooth the flow of traffic in a controlled manner, and thus reduce the energy footprint. The research combines mathematics, control theory, machine learning, and transportation engineering. The project includes four universities and engages industry and government partners. The project will also engage students and community stakeholders, including State and Federal transportation agencies and CAV manufacturers.Specifically, the technical contributions enabling traffic smoothing and reduction in the environmental footprint include new mean-field optimal control formulations for sparse control settings where only a subset of vehicles are CAVs and can be controlled. Investigators will develop data-driven control algorithms based on deep reinforcement learning designed to enable control in settings where analytical approaches to derive explicit controllers are too complex (e.g., due to multi-lane, ramps, and high variation of human driving styles). They will also develop tools based on Satisfiability Modulo Convex optimization to enable safety and robustness of these controllers. The approach will first be validated using microsimulation tools to assess their efficiency and their validity. Once validated in simulation, the project will then field test the algorithm with manned vehicles following real-time control commands of the system, executed by 100 human drivers following control signals communicated via a phone app with target speeds and lanes. After which, the system will be tested with up to 20 CAVs inserted onto a freeway stretch in the Transition to Practice component of the project.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.
Project Terms:
Algorithms; Automation; Automobile Driving; Award; base; Cities; Communities; Complex; Congestive; control theory; cost; Data; deep reinforcement learning; design; Engineering; Evaluation; field study; Formulation; Foundations; Government; Human; improved; Industry; Infrastructure; Machine Learning; Manufacturer Name; Mathematics; Mission; Modeling; Names; Ramp; Research; Research Personnel; Safety; Signal Transduction; simulation; Speed; Stretching; Students; System; Telephone; Testing; Time; tool; Transportation; United States; Universities; Variant; Work

Details

Contact PI / Project Leader Information:
Name:  PICCOLI, BENEDETTO
Other PI Information:
Not Applicable
Awardee Organization:
Name:  RUTGERS THE STATE UNIVERSITY OF NEW JERSEY CAMDEN
City:  CAMDEN    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  01
Other Information:
Fiscal Year: 2018
Award Notice Date: 13-Sep-2018
DUNS Number: 625216556
Project Start Date: 01-Jan-2019
Budget Start Date:
CFDA Code: 47.070
Project End Date: 31-Dec-2021
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2018:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2018 NSF

National Science Foundation

$230,000

Results

i

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