Award Abstract # 1932452
CPS: Small: Cyber-Physical Phases of Mixed Traffic with Modular & Autonomous Vehicles: Dynamics, Impacts and Management

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: UNIVERSITY OF SOUTH FLORIDA
Initial Amendment Date: August 1, 2019
Latest Amendment Date: August 1, 2019
Award Number: 1932452
Award Instrument: Standard Grant
Program Manager: Yueyue Fan
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: January 1, 2020
End Date: February 28, 2023 (Estimated)
Total Intended Award Amount: $500,000.00
Total Awarded Amount to Date: $500,000.00
Funds Obligated to Date: FY 2019 = $109,606.00
History of Investigator:
  • Xiaopeng Li (Principal Investigator)
    xli2485@wisc.edu
Recipient Sponsored Research Office: University of South Florida
4202 E FOWLER AVE
TAMPA
FL  US  33620-5800
(813)974-2897
Sponsor Congressional District: 15
Primary Place of Performance: University of South Florida
Eng 207, 4202 E. Fowler Avenue
Tampa
FL  US  33620-0010
Primary Place of Performance
Congressional District:
15
Unique Entity Identifier (UEI): NKAZLXLL7Z91
Parent UEI:
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 152E, 7918, 7923
Program Element Code(s): 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Emerging technologies in communications and vehicle technologies will allow future autonomous vehicles to be platooned together with wireless communications (cyber-connected) or physically forming an actual train (physically-connected). When physically connected, vehicles may dock to and undock from each other en-route when vehicles are still moving. While such platooning can potentially offer substantial societal benefits in safety, mobility and environmental friendliness, their emergence also challenges the classic traffic flow models that do not account for the state that vehicles can have very short to no gaps from each other. And yet, classic traffic flow models are being used for all traffic simulations for assessment on safety, mobility and environment. This project aims to expand classic highway traffic flow models to account for states where vehicles can be very close to or even physically connected with each other. These new models will help stakeholders plan and manage future transportation systems and supply the engineering curriculum with new methods, tools, and experimental platforms oriented towards future smart urban systems.

The objectives of this research are (1) to gain new knowledge on the impacts of the emerging new states in highway traffic dynamics in both ideal (e.g., with zero sensor errors and delay, infinite communication range, and infinite computational power ) and realistic ( e.g., with sensor noise, communication delay and computational limits) operational conditions, (2) to devise mechanisms and managing strategies to properly regulate the multi-state mixed traffic for its best performance, and (3) to quantify the key components of the models and systems via both full-scale and reduced-scale testbeds. These models will provide theoretical insights on the upper-bound performance of a mixed traffic system in ideal operational conditions. Then realistic cyber-physical constraints will be incorporated into the highway system and agent-based simulations will be conducted to understand how the system performance will be compromised due to these real-world cyber-physical constraints. Various management strategies will also be explored via both decentralized (e.g., each individual vehicle making decisions on its own) and centralized (e.g., all vehicles controlled or coordinated by a central operator) control strategies for offsetting the performance of a transportation system closer to the theoretical upper bound. Finally, field experiments on both multi-scale testbeds will be conducted to validate the key components of the theorems and models.

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.

(Showing: 1 - 10 of 14)
Li, Qianwen and Chen, Zhiwei and Li, Xiaopeng "A Review of Connected and Automated Vehicle Platoon Merging and Splitting Operations" IEEE transactions on intelligent transportation systems , 2022 https://doi.org/10.1109/TITS.2022.3193278 Citation Details
Li, Qianwen and Li, Xiaopeng "Generalized Fundamental Diagram with Implications of Congestion Mitigation" Journal of Transportation Engineering, Part A: Systems , v.148 , 2022 https://doi.org/10.1061/JTEPBS.0000658 Citation Details
Li, Qianwen and Li, Xiaopeng "Trajectory planning for autonomous modular vehicle docking and autonomous vehicle platooning operations" Transportation Research Part E: Logistics and Transportation Review , v.166 , 2022 https://doi.org/10.1016/j.tre.2022.102886 Citation Details
Li, Qianwen and Yao, Handong and Li, Xiaopeng "A matched case-control method to model car-following safety" Transportmetrica A: Transport Science , 2022 https://doi.org/10.1080/23249935.2022.2055198 Citation Details
Pei, Mingyang and Lin, Peiqun and Du, Jun and Li, Xiaopeng and Chen, Zhiwei "Vehicle dispatching in modular transit networks: A mixed-integer nonlinear programming model" Transportation Research Part E: Logistics and Transportation Review , v.147 , 2021 https://doi.org/10.1016/j.tre.2021.102240 Citation Details
Shi, Xiaowei and Li, Xiaopeng "Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration" Transportation Research Part B: Methodological , v.150 , 2021 https://doi.org/10.1016/j.trb.2021.06.011 Citation Details
Shi, Xiaowei and Li, Xiaopeng "Empirical study on car-following characteristics of commercial automated vehicles with different headway settings" Transportation Research Part C: Emerging Technologies , v.128 , 2021 https://doi.org/10.1016/j.trc.2021.103134 Citation Details
Shi, Xiaowei and Li, Xiaopeng "Operations Design of Modular Vehicles on an Oversaturated Corridor with First-in, First-out Passenger Queueing" Transportation Science , 2021 https://doi.org/10.1287/trsc.2021.1074 Citation Details
Shi, Xiaowei and Wang, Zhen and Li, Xiaopeng and Pei, Mingyang "The effect of ride experience on changing opinions toward autonomous vehicle safety" Communications in Transportation Research , v.1 , 2021 https://doi.org/10.1016/j.commtr.2021.100003 Citation Details
Shi, Xiaowei and Zhao, Dongfang and Yao, Handong and Li, Xiaopeng and Hale, David K. and Ghiasi, Amir "Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation (HIGH-SIM)" Communications in Transportation Research , v.1 , 2021 https://doi.org/10.1016/j.commtr.2021.100014 Citation Details
Wang, Zhen and Zhao, Xiangmo and Chen, Zhiwei and Li, Xiaopeng "A dynamic cooperative lane-changing model for connected and autonomous vehicles with possible accelerations of a preceding vehicle" Expert Systems with Applications , v.173 , 2021 https://doi.org/10.1016/j.eswa.2021.114675 Citation Details
(Showing: 1 - 10 of 14)

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page