Effect of Neural Controller on Adaptive Cruise Control

Kuyumcu, Arden; Şengör, Neslihan Serap

doi:10.1007/978-3-319-44781-0_61

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…54 With the development of artificial intelligent, different heuristic optimization techniques have been applied to ACC control. Kuyumcu and S xengo¨r 55 proposed a feed-forward ANN, which achieved fast response and narrow vehicular inter-distance. Zhang and Zhang 56 used neural network to optimize a MPC-based Multiobjective Adaptive Cruise Control (MO-ACC) system.…”

Section: Hierarchical Controllermentioning

confidence: 99%

Researches on Adaptive Cruise Control system: A state of the art review

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

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Section: Hierarchical Controllermentioning

confidence: 99%

Researches on Adaptive Cruise Control system: A state of the art review

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Several control strategies presented in the literature have been applied to ACC, such as intelligent control (Kuyumcu and Sengor (2016)), sliding mode control (Ganji et al (2014)) and model predictive control (MPC) (Li et al (2017)), (Magdici and Althoff (2017)). Among these controllers, the MPC has been one of the most discussed.…”

Section: Introductionmentioning

confidence: 99%

Safe Adaptive Cruise Control with Control Barrier Function and Smith Predictor

Chinelato

Angélico

2020

Anais Do Congresso Brasileiro De Automática 2020

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This work presents the development of Adaptive Cruise Control (ACC) applied to a vehicle. The ACC tracks a predefined controlled vehicle cruise speed, however when a leading vehicle with lower speed is encountered, the ACC must adapt the controlled vehicle speed to maintain a safe distance between the vehicles. The control strategy applied combines Control Lyapunov Function (CLF), related to performance/stability objectives and Control Barrier Function (CBF), related to safety conditions represented by a safe set. CLF and CBF are integrated with Quadratic Programming (QP) and a relaxation is used to make performance/stability objectives as a soft constraint and safety conditions as a hard constraint. The system model is based on a vehicle available at EPUSP and presents an input time-delay, that can degrade performance and stability. The input delay is compensated with a Smith Predictor. The initial results were obtained through numerical simulations and, in the future, the scheme will be implemented in the vehicle. The numerical simulations indicate that the proposed controller respect the performance/stability objectives and the safety conditions.

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“…Publications describing the use of neural networks [15][16][17][18][19][20][21][22][23][24][25][26][27] mostly apply them as a model of driver behavior [15,[19][20][21][22][23]27], which is connected to the ACC assistance system. The aim is to create a driver model imitating the driver's activity based on real data and The problem of adaptive cruise control (ACC) can be transformed into the problem of optimal tracking control for complex nonlinear systems.…”

mentioning

confidence: 99%

“…The aim is to create a driver model imitating the driver's activity based on real data and The problem of adaptive cruise control (ACC) can be transformed into the problem of optimal tracking control for complex nonlinear systems. This task is currently solved by different methods using different methods-for example with experience playback technology [14], neural networks [15][16][17][18][19][20][21][22][23][24][25][26][27][28], fuzzy-neural approaches [29][30][31], adaptive algorithms [32,33], or combinations thereof [34][35][36] or modern function approximation techniques together with gradient learning algorithms [37].…”

mentioning

confidence: 99%

“…Another group of publications [16,18,25,26,28] is the application of neural networks in ACC internal control loops and have the function of a predictive control algorithm that integrates the ability of artificial neural networks to mimic vehicle characteristics and predictive model control to minimize quadratic error between future reference trajectories and predicted outputs. In many cases, two separate control loops are used: an outer loop based on a decision algorithm and a continuous controller that gives the inner loop a reference speed to maintain a safe distance from the vehicle in front.…”

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confidence: 99%

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Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

et al. 2021

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This article deals with the use of neural networks for estimation of deceleration model parameters for the adaptive cruise control unit. The article describes the basic functionality of adaptive cruise control and creates a mathematical model of braking, which is one of the basic functions of adaptive cruise control. Furthermore, an analysis of the influences acting in the braking process is performed, the most significant of which are used in the design of deceleration prediction for the adaptive cruise control unit using neural networks. Such a connection using artificial neural networks using modern sensors can be another step towards full vehicle autonomy. The advantage of this approach is the original use of neural networks, which refines the determination of the deceleration value of the vehicle in front of a static or dynamic obstacle, while including a number of influences that affect the braking process and thus increase driving safety.

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Effect of Neural Controller on Adaptive Cruise Control

Cited by 6 publications

References 9 publications

Researches on Adaptive Cruise Control system: A state of the art review

Researches on Adaptive Cruise Control system: A state of the art review

Safe Adaptive Cruise Control with Control Barrier Function and Smith Predictor

Application of Artificial Neural Networks to Streamline the Process of Adaptive Cruise Control

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