Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

Yoon, Seungje; Jeon, Hyeongseok; Kum, Dongsuk

doi:10.1109/tits.2019.2928217

Cited by 28 publications

(8 citation statements)

References 24 publications

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“…Apart from these main movements, the range of applications in engineering is immense. From balancing walking robots [134], hanging crane loads [110], and cruise control for heavy duty trucks [62,140], to optimizing buffering and quality in video streaming [138]. Even for path tracking of underwater robots, MPC was applied [116].…”

Section: Further Applicationsmentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

386

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Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

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Section: Further Applicationsmentioning

confidence: 99%

Review on model predictive control: an engineering perspective

Schwenzer

Bergs

et al. 2021

Int J Adv Manuf Technol

386

View full text Add to dashboard Cite

show abstract

“…The intelligent railway signal machine adopts computer and network communication technology to replace the signal lighting and relay combination in the current railway station signal control system, and realizes the network control and management of the outdoor signal machine through the intelligent design of the signal light. Railway signal lighting control system [1]. The system has the characteristics of simple structure, safety, energy saving and convenient maintenance.…”

Section: Introductionmentioning

confidence: 99%

Railway vehicle network control system based on artificial intelligence

Gao

2023

2022 Workshop on Electronics Communication Engineering

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The in-vehicle network communication system and the in-vehicle network control system are independent and inseparable, and the network performance and control performance affect and restrict each other. The purpose of this paper is to study the optimal design of a network control system for railway vehicles based on artificial intelligence. This article is a project actually implemented relying on the railway vehicle network control system in M province. This paper selects the MVB network that conforms to the IEC61375-3 standard through the analysis and comparison of several mainstream communication networks in the rail transit industry. In the process of network topology design, the network system is divided into two network segments, the vehicle network and the train network, through the rational use of repeaters. There are a total of 160 CCU source ports and sink ports. In the process of designing the network control system, we have fully considered various redundancy design schemes, such as master control redundancy, line redundancy, key signal redundancy, hard cable signal redundancy, etc., to ensure the stability and reliability of the entire system. In order to achieve the design goal of subway vehicles mainly based on network control and supplemented by hard line control.

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“…Active safety systems, such as automatic emergency braking (AEB), forward collision warning, and blind spot detection systems have been successfully introduced in the automotive market [1], [2]. Moreover, the advanced active safety systems, e.g., collision evasive lateral maneuvre systems (CELM), automatic emergency steering (AES) and minimal risk maneuver (MRM), have been developed and standardized [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Strategy Decision Integrating Convolution Neural Network With Threat Assessment and Motion Prediction for Automatic Evasive Steering

Lee,

Song

2023

IEEE Access

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In this paper, the strategy decision algorithm for automatic evasive steering (AES) integrating a convolution neural network (CNN) with a physics-based threat assessment is proposed. Five collision avoidance or mitigation strategies, including evasive steering, lane change, and steering to shoulder stop are considered for the strategy decision. Although there are many model-based or data-driven approaches for collision avoidance in the literature, a new decision method integrating data-driven classification based on CNN with both threat assessment and prediction techniques is proposed to improve reliability as well as accuracy. First, a set of abstracted images in a bird eye's view including the threat assessment and trajectory prediction information are generated. More specifically, a few collision indexes and interaction multiple model-unscented Kalman filter are used respectively for threat assessment and prediction. Once a stack of the images so called predicted semantic map corresponding to each collision avoidance strategy are generated, the decision classification based on CNN follows to choose an appropriate strategy for AES. Finally, the proposed decision algorithm is trained and validated through typical safe scenario data coming from field operation tests and safety-critical scenario data via simulations.

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Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

Cited by 28 publications

References 24 publications

Review on model predictive control: an engineering perspective

Review on model predictive control: an engineering perspective

Railway vehicle network control system based on artificial intelligence

Data-Driven Strategy Decision Integrating Convolution Neural Network With Threat Assessment and Motion Prediction for Automatic Evasive Steering

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