Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

Li, Shengbo Eben; Zheng, Yang; Li, Keqiang; Wu, Yujia; Hedrick, J. Karl; Gao, Feng; Zhang, Hongwei

doi:10.1109/mits.2017.2709781

Cited by 362 publications

(192 citation statements)

References 61 publications

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“…One important application is to develop cooperative control strategies for multiple connected vehicles based on local information to guarantee a certain global coordination, such that the traffic efficiency and road safety are improved. This is called platoon control of connected vehicles [2], and is also known as cooperative adaptive cruise control (CACC) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Also, some proof-ofconcept demonstrations have been performed in the projects of GCDC [3], SARTRE [12] and Energy-ITS [13]. The interested reader is referred to [2] for a recent overview.…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of vehicle-to-vehicle (V2V) communications, such as DSRC and VANETs [14], one recent research focus of platooning is on developing scalable analysis and synthesis methods for the cooperation of largescale platoons with various communication topologies [2]. For instance, an explicit stabilizing region of linear feedback gains was derived for homogenous platoons with a large class of communication topologies [15].…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Control of Heterogeneous Connected Vehicles with Directed Acyclic Interactions

Zheng

Bian

2021

IEEE Intell. Transport. Syst. Mag.

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Cooperation of multiple connected vehicles has the potential to benefit the road traffic greatly. In this paper, we consider analysis and synthesis problems of the cooperative control of a platoon of heterogeneous connected vehicles with directed acyclic interactions (characterized by directed acyclic graphs). In contrast to previous works that view heterogeneity as a type of uncertainty, this paper directly takes heterogeneity into account in the problem formulation, allowing us to develop a deeper understanding of the influence of heterogeneity on the collective behavior of a platoon of connected vehicles. Our major strategies include an application of the celebrated internal model principle and an exploitation of lower-triangular structures for platoons with directed acyclic interactions. The major findings include: 1) we explicitly highlight the tracking ability of heterogeneous platoons, showing that the followers can only track the leader's spacing and velocity; 2) we analytically derive a stability region of feedback gains for platoons with directed acyclic interactions; 3) and consequently we propose a synthesis method based on the solution to an algebraic Riccati equation that shares the dimension of single vehicle dynamics. Numerical experiments are carried out to validate the effectiveness of our results.

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Section: Introductionmentioning

confidence: 99%

“…Also, some proof-ofconcept demonstrations have been performed in the projects of GCDC [3], SARTRE [12] and Energy-ITS [13]. The interested reader is referred to [2] for a recent overview.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Control of Heterogeneous Connected Vehicles with Directed Acyclic Interactions

Zheng

Bian

2021

IEEE Intell. Transport. Syst. Mag.

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“…Cooperative automation of connected vehicles is expected to be realized using powerful computing ability (e.g., cloud computation) and advanced communication technology (e.g., V2V, V2I) [1]. In recent years, coordinating multiple connected vehicles has received considerable research attention due to its potential to improve traffic efficiency and safety [2], [3]. One main challenge in cooperative automation is to ensure that coupling constraints among connected vehicles are satisfied and the computation is efficient [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…LaValle et al established a solution mapping between the planning problem and network-flow, and then applied integer linear programming to optimize the objective [10]. In short, to deal with coupling constraints, the distributed formation of the problem needs to be assigned appropriately, i.e., the topology of platoons and the priorities of the priority approach [3], [8], [11]. Another intuitive way of executing a cooperative task is to establish a centralized system to optimize the global decision space of all vehicles.…”

Section: Introductionmentioning

confidence: 99%

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

Wang

Zheng

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method.

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An adaptive cascade predictive control strategy for connected and automated vehicles

Landolfi

Natale

2023

Adaptive Control & Signal

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SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

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Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

Cited by 362 publications

References 61 publications

Cooperative Control of Heterogeneous Connected Vehicles with Directed Acyclic Interactions

Cooperative Control of Heterogeneous Connected Vehicles with Directed Acyclic Interactions

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

An adaptive cascade predictive control strategy for connected and automated vehicles

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