Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability

Wang, Jiawei; Zheng, Yang; Chen, Chaoyi; Xu, Qing; Li, Keqiang

doi:10.1109/tits.2021.3118021

Cited by 66 publications

(74 citation statements)

References 48 publications

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“…Before presenting the methodology, we need to investigate two fundamental controltheoretic properties of the mixed traffic system, controllability and observability, which are essential to establish data-driven predictive control [25]. Our previous work on LCC has investigated the special case with only one CAV [10]. In the next section, we generalize these results to the case with possibly multiple CAVs and HDVs coexisting (see Fig.…”

Section: Linearized State-space Model Of Mixed Traffic Systemmentioning

confidence: 99%

“…In this paper, we focus on the recent LCC framework [10] and design safe and optimal control strategies for CAVs to smooth mixed traffic flow. Our method requires no prior knowledge of HDVs' car-following dynamics.…”

Section: B Contributionsmentioning

confidence: 99%

“…In this section, we first introduce the nonlinear modeling of HDVs' car-following behavior, and then present the linearized dynamics of a general mixed traffic system under the LCC framework [10].…”

Section: Theoretical Modeling Frameworkmentioning

confidence: 99%

“…One extension of CACC to mixed traffic is Connected Cruise Control (CCC) [9], in which one single CAV at the tail makes its control decisions by exploiting the information of multiple HDVs ahead. Another recent extension is Leading Cruise Control (LCC) that incorporates the motion of HDVs ahead and behind [10].…”

Section: Introductionmentioning

confidence: 99%

“…The recent notion of LCC [10] focuses on general open straight road scenarios and has provided further insight into CAV control in mixed traffic: one single CAV can not only adapt to the downstream traffic flow consisting of its preceding HDVs (as a follower), but also improve the upstream traffic performance by actively leading the motion of its following HDVs (as a leader). This explicit consideration of a CAV as both a leader and a follower greatly enhances its capability in smoothing mixed traffic flow, as demonstrated both empirically and theoretically in [10]. One challenge is to design LCC strategies with safety guarantees in smoothing traffic flow when the traffic model is not known.…”

Section: Introductionmentioning

confidence: 99%

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DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Wang¹,

Zheng²,

Li³

et al. 2022

Preprint

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For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies. They require explicit knowledge of car-following dynamics of human-driven vehicles that are non-trivial to identify accurately. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven non-parametric strategy, called DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control), to achieve safe and optimal control of CAVs in mixed traffic. We first utilize Willems' fundamental lemma to obtain a data-centric representation of mixed traffic behavior. This is justified by rigorous analysis on controllability and observability properties of mixed traffic. We then employ a receding horizon strategy to solve a finite-horizon optimal control problem at each time step, in which input/output constraints are incorporated for collision-free guarantees. Numerical experiments validate the performance of DeeP-LCC compared to a standard predictive controller that requires an accurate model. Extensive nonlinear traffic simulations further confirm its great potential on improving traffic efficiency, driving safety, and fuel economy.

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Section: Linearized State-space Model Of Mixed Traffic Systemmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

Section: Theoretical Modeling Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Wang¹,

Zheng²,

Li³

et al. 2022

Preprint

Self Cite

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Safe and robust data‐driven cooperative control policy for mixed vehicle platoons

Lan

Zhao

2022

Intl J Robust & Nonlinear

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This article considers mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). The uncertainties and randomness in human driving behaviors highly affect the platoon safety and stability. However, most existing control strategies are either for platoons of pure AVs, or for special formations of mixed platoons with known HV models. This article addresses the control of mixed platoons with more general formations and unknown HV models. An innovative data-driven policy learning strategy is proposed to design the controllers for AVs based on vehicle-to-vehicle (V2V) communications. The policy learning strategy is embedded with the constraints of control input, inter-vehicular distance error and V2V communication topology. The strategy establishes a safe and robustly stable mixed platoon using prescribed communication topologies. The design efficacy is verified through simulations of a mixed platoon with different communication topologies and leader velocity profiles.

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Robust control scheme for the mixed platoon system with time-varying information topologies and inaccurate state information

Gong,

Zhu

2024

Nonlinear Dyn

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Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability

Cited by 66 publications

References 48 publications

DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Safe and robust data‐driven cooperative control policy for mixed vehicle platoons

Robust control scheme for the mixed platoon system with time-varying information topologies and inaccurate state information

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