Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control

Gao, Bingzhao; Cai, Kunyang; Qu, Ting; Hu, Yunfeng; Chen, Hong

doi:10.1109/tvt.2020.3020335

Cited by 87 publications

(21 citation statements)

References 26 publications

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“…In [14], the authors take into account the characteristics of wireless communication links, such as the sampling delays, and present a network-aware CACC approach. In other works [15]- [24], researchers have designed a variety of ACC/CACC control solutions by combining different mechanisms, such as multi-modeling [15], driving style recognition [16], direct yaw moment control [17], acceleration/control feedforward [18], linear quadratic regulator (LQR) [19], and information-delay compensation mechanisms [20]- [24]. Additionally, some researchers incorporate parametric uncertainties into the vehicle dynamics and aim to design robust linear feedback controllers for vehicle platooning by leveraging the classical Kharitonov theorem and the Hurwitz criterion [28].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Optimal control can be further divided into two categories, model predictive control and rolling horizon control, according to the time horizon for control implementation. In fact, many researchers have leveraged model predictive control (MPC) technique to develop platoon-oriented ACC and CACC controllers like the aforementioned works [11], [13], [16], [17]. In [46], the authors propose a distributed MPC platoon control approach by considering unidirectional topologies.…”

Section: A Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Zhou

Sheng

Duan

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) Manuscript

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Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Zhou

Sheng

Duan

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Aljaafreh et al [19] and Chen and Chen [20] selected acceleration and speed of the FV as characteristics to identify driving style, while Li et al [15] chose acceleration and time headway. Gao et al [21] used a group of variables, e.g., relative speed, time headway, and jerk, to reflect the differences in driving styles. Sun et al [22] utilized inter-vehicle distance, speed, and acceleration/deceleration as car-following variables to analyze driving style.…”

Section: Driving Style Cluster Analysismentioning

confidence: 99%

Research on Car-Following Model considering Driving Style

Wang

Yang

Wang

et al. 2022

Mathematical Problems in Engineering

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In this paper, a car-following model considering various driving styles is constructed to fulfill the personalized needs of different users of autonomous vehicles. First, according to a set of selection rules, car-following events are selected from the Next Generation Simulation (NGSIM) dataset, and then through an unsupervised machine learning method, the extracted data are divided into two styles, i.e., conservative and aggressive. Statistical analysis is then conducted to analyze the differences in vehicle speed, acceleration, desired time headway, and so on between both driving styles. Based on the analysis, a car-following model based on model predictive control is designed. Experimental results from testing data show that the proposed car-following models demonstrate different driving styles in terms of safety, comfort, and effectiveness. The conservative driving model is safer and more comfortable than the radical driving model, but the driving efficiency is low.

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“…In order to determine the analytical solution of the problem, the MPC optimization problem needs to be transformed into a constrained quadratic program [29], as shown in the following formula:…”

Section: Problem Solvingmentioning

confidence: 99%

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

Chen¹,

Guo²,

Guo³

et al. 2021

Applied Sciences

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In a cut-in scenario, traditional adaptive cruise control usually cannot effectively identify the cut-in vehicle and respond to it in advance. This paper proposes an adaptive cruise control (ACC) strategy based on the MPC algorithm for cut-in scenarios. A finite state machine (FSM) is designed to manage vehicle control in different cut-in scenarios. For a cut-in scenario, a method to identify and quantify the possibility of cut-in of a vehicle is proposed. At the same time, a safety distance model of the cut-in vehicle is established as the basis for the state transition of the finite state machine. Taking the quantified cut-in possibility of a vehicle as a reference, the model predictive control (MPC) algorithm, which considers the constraints of driving safety and comfort, is used to realize coordinated control of the host vehicle and the cut-in vehicle. Simulink–Carsim simulation studies show that the ACC strategy for a cut-in scenario can effectively identify a cut-in vehicle and quantify the possibility of cut-in of the vehicle. Faced with a cut-in vehicle, the host vehicle using the ACC strategy can brake several seconds early and switch the following target to the cut-in vehicle. Meanwhile, the acceleration and jerk of the host vehicle changes within a reasonable range, which ensures driving safety and comfort.

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Personalized Adaptive Cruise Control Based on Online Driving Style Recognition Technology and Model Predictive Control

Cited by 87 publications

References 26 publications

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Research on Car-Following Model considering Driving Style

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

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