Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models

Yuguang Wei, Cafer Avcı, Jiangtao Liu, Baloka Belezamo, Nizamettin Aydın, Pengfei(Taylor) Li, Xuesong Zhou

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.

Original languageEnglish (US)
Pages (from-to)102-129
Number of pages28
JournalTransportation Research Part B: Methodological
Volume106
DOIs
StatePublished - Dec 1 2017

Fingerprint

Dynamic programming
Railroad cars
programming
Trajectories
optimization model
transportation system
scheduling
communication technology
traffic
infrastructure
time
communication
interaction
Communication
Integer programming
Collision avoidance
Wave propagation
Computational complexity
Lead
Scheduling

Keywords

  • Autonomous vehicle
  • Car-following model
  • Traffic flow management
  • Vehicle trajectory optimization

ASJC Scopus subject areas

  • Transportation

Cite this

Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models. / Wei, Yuguang; Avcı, Cafer; Liu, Jiangtao; Belezamo, Baloka; Aydın, Nizamettin; Li, Pengfei(Taylor); Zhou, Xuesong.

In: Transportation Research Part B: Methodological, Vol. 106, 01.12.2017, p. 102-129.

Research output: Contribution to journalArticle

Wei, Yuguang ; Avcı, Cafer ; Liu, Jiangtao ; Belezamo, Baloka ; Aydın, Nizamettin ; Li, Pengfei(Taylor) ; Zhou, Xuesong. / Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models. In: Transportation Research Part B: Methodological. 2017 ; Vol. 106. pp. 102-129.
@article{0b86931ad7f84a098f73e5707cbd0b64,
title = "Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models",
abstract = "Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.",
keywords = "Autonomous vehicle, Car-following model, Traffic flow management, Vehicle trajectory optimization",
author = "Yuguang Wei and Cafer Avcı and Jiangtao Liu and Baloka Belezamo and Nizamettin Aydın and Pengfei(Taylor) Li and Xuesong Zhou",
year = "2017",
month = "12",
day = "1",
doi = "10.1016/j.trb.2017.10.012",
language = "English (US)",
volume = "106",
pages = "102--129",
journal = "Transportation Research, Series B: Methodological",
issn = "0191-2615",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models

AU - Wei, Yuguang

AU - Avcı, Cafer

AU - Liu, Jiangtao

AU - Belezamo, Baloka

AU - Aydın, Nizamettin

AU - Li, Pengfei(Taylor)

AU - Zhou, Xuesong

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.

AB - Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation transportation system with autonomous and connected vehicles. Based on a space-time lattice, we present a set of integer programming and dynamic programming models for scheduling longitudinal trajectories, where the goal is to consider both system-wide safety and throughput requirements under supports of various communication technologies. Newell's simplified linear car following model is used to characterize interactions and collision avoidance between vehicles, and a control variable of time-dependent platoon-level reaction time is introduced in this study to reflect various degrees of vehicle-to-vehicle or vehicle-to-infrastructure communication connectivity. By adjusting the lead vehicle's speed and platoon-level reaction time at each time step, the proposed optimization models could effectively control the complete set of trajectories in a platoon, along traffic backward propagation waves. This parsimonious multi-vehicle state representation sheds new lights on forming tight and adaptive vehicle platoons at a capacity bottleneck. We examine the principle of optimality conditions and resulting computational complexity under different coupling conditions.

KW - Autonomous vehicle

KW - Car-following model

KW - Traffic flow management

KW - Vehicle trajectory optimization

UR - http://www.scopus.com/inward/record.url?scp=85034615540&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034615540&partnerID=8YFLogxK

U2 - 10.1016/j.trb.2017.10.012

DO - 10.1016/j.trb.2017.10.012

M3 - Article

AN - SCOPUS:85034615540

VL - 106

SP - 102

EP - 129

JO - Transportation Research, Series B: Methodological

JF - Transportation Research, Series B: Methodological

SN - 0191-2615

ER -