A Multi-Objective Model Predictive Control for Vehicle Adaptive Cruise Control System Based on a New Safe Distance Model

Liu, Zhenze; Yuan, Qing; Nie, Guangming; Tian, Yantao

doi:10.1007/s12239-021-0044-0

Cited by 30 publications

(11 citation statements)

References 14 publications

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“…e primary goal of solving the synchronization problem for automotive embedded systems is to propose a synchronization model that can adapt to the system [6]. Liu et al abstracted the end-to-end computing process into a data flow graph, and set the model as directed, thereby constructing a DAG model; and then based on this model, research is done on the end-to-end synchronous real-time scheduling of automotive embedded systems based on CAN bus [7]. Based on the DAG model, Sivakumar et al studied the synchronization scheduling optimization problem of time-triggered FlexRay static segments [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

Wang¹,

He²

2022

Journal of Control Science and Engineering

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In order to improve the performance of the automotive active safety system, the author proposes a distributed embedded ACCS hardware loop simulation system. The automotive adaptive cruise control system (ACCS) is a completely distributed embedded control system. The author adopts the method of hardware loop simulation, an ARM-based automotive ACCS simulation platform is designed, and the hardware structure and software design of the platform are introduced. The simulation results show that the performance of single-node and double-node responses to step change is basically the same. There is a network in the two-node control loop, the carrying capacity and communication bandwidth of the network are limited, which will inevitably cause information collision and retransmission, which will inevitably cause a delay in the information transmission process, with a delay of about 4.5 seconds. The system opens up the design idea of ACCS and also lays a foundation for future research in related directions, which has a certain enlightenment effect.

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Section: Literature Reviewmentioning

confidence: 99%

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

Wang¹,

He²

2022

Journal of Control Science and Engineering

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“…Because MPC has the ability for real-time operation and MO optimization, it is used to calculate the optimal control variable. The ACC system state-space equation is given as follows 8), ( 9), (10), and ( 12) are integrated together by employing a linear weighting method as in (Liu et al, 2021;Mao et al, 2021) where p = 10 is the prediction time horizon, and m = 1 denotes the control horizon. ŷp (k + ijk) represents the observed value at time k. y r (k + i) is the reference trajectory at time k + i. u(k + i) is the control variable at time k + i.…”

Section: Mpc For Mo-accmentioning

confidence: 99%

Adaptive cruise control method based on hierarchical control and multi-objective optimization

Piao

Gao

Yang

et al. 2022

Transactions of the Institute of Measurement and Control

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Considering the perception of a driver, driving safety, and riding comfort, an adaptive cruise control (ACC) method based on a multi-objective (MO) real-time optimization control algorithm is proposed. The ACC system can be divided into various working modes according to the driver’s perception, as well as the relative state of the host vehicle and the preceding vehicle. To distinguish the driver’s intention in the rapid acceleration, strong deceleration, steady, and larger clearance modes, a fuzzy tool is proposed to solve the problem. To maintain the inter-clearance, velocity, and jerking action in a reasonable region, an MO control algorithm based on model predictive control was used to obtain the optimal control vectors. Moreover, the control vector increment is restricted to suppressing the fluctuation caused by switching among various modes. Simulations were conducted for various scenarios to verify the effectiveness of the ACC method. The simulation results indicated that the driver’s comfort and fuel economy improved by 35.3% and 16.6%, respectively, compared with the linear quadratic algorithm in a complicated driving environment. Various simulation results demonstrated that the MO-ACC controller can reduce fuel consumption while barely sacrificing riding comfort or tracking performance, as compared to the linear quadratic controller.

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“…where ∆x k, re f is the reference inter-vehicular distance of the k-th vehicle (i.e., the controlled vehicle); ∆x k,,0 is a certain initial space gap; h and v k are the time headway and the velocity of the k-th vehicle. This value is often found among the state variables along with the relative velocity between lead and ego vehicles and are the main parameters minimized when using MPC in the ACC framework [29,[90][91][92][93][94][95][96][97][98][99][100][101][102][103][104].…”

Section: Cost Function In Mpc Problemsmentioning

confidence: 99%

“…In these scenarios, inter-vehicular distance IVD is often bounded to be lower than a certain value (i.e., considering road occupancy for minimizing the impact on the traffic) and higher than a safety distance. Along with the distance, operational ranges are also provided to velocity, acceleration and acceleration rate to ensure passenger comfort [29,44,50,[90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122].…”

Section: Constraints In Mpc Problemsmentioning

confidence: 99%

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

et al. 2021

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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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A Multi-Objective Model Predictive Control for Vehicle Adaptive Cruise Control System Based on a New Safe Distance Model

Cited by 30 publications

References 14 publications

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

Adaptive cruise control method based on hierarchical control and multi-objective optimization

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

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