Paper Info
Title
Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability
Abstract
Connected and autonomous vehicles (CAVs) have great potential to improve road transportation systems. Most existing strategies for CAVs' longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs' behaviors on upstream traffic flow. In this paper, we introduce a notion of Leading Cruise Control (LCC), in which the CAV maintains car-following operations adapting to the states of its preceding vehicles, and also aims to lead the motion of its following vehicles. Specifically, by controlling the CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream traffic flow actively. We first present the dynamical modeling of LCC, with a focus on three fundamental scenarios: car-following, free-driving, and Connected Cruise Control. Then, the analysis of controllability, observability, and head-to-tail string stability reveals the feasibility and potential of LCC in improving mixed traffic flow performance. Extensive numerical studies validate that the capability of CAVs in dissipating traffic perturbations is further strengthened when incorporating the information of the vehicles behind into the CAVs' control.
Year
DOI
Venue
2022
10.1109/TITS.2021.3118021
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Vehicle dynamics, Topology, Cruise control, Controllability, Perturbation methods, Observability, Numerical stability, Connected and autonomous vehicle, cruise control, mixed traffic flow, controllability, string stability
Journal
23
Issue
ISSN
Citations 
8
1524-9050
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jiawei Wang100.34
Yang Zheng226718.67
Chaoyi Chen322.42
Qing Xu4105.97
Keqiang Li558352.39