Stochastic predictive control for lane keeping assistance systems using a linear time-varying model

Liu, Changchun; Carvalho, Ashwin; Schildbach, Georg; Hedrick, J. Karl

doi:10.1109/acc.2015.7171850

Cited by 22 publications

(10 citation statements)

References 16 publications

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“…MPC has already been applied in several automotive control applications, including traction control [265], braking and steering [266,267], lane keeping [268] etc. Model predictive techniques have also been applied to the trajectory tracking problem in various works [269][270][271][272][273][274][275].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Model Predictive Controlmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…The future response of the system is enumerated according to the model [ 67 ]. In the context of automotive control applications, MPC have been applied to braking and steering [ 68 , 69 ], traction control [ 70 ], lane-keeping [ 71 ]. MPC finds an optimal path by solving an optimization problem for each timestep and produces an actuator signal depending upon the finite horizon.…”

Section: Architecturementioning

confidence: 99%

System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Azam

Munir

Sheri

et al. 2020

Sensors

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In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.

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“…At the ith stage of the control policy, the control input ui is selected as the feedback control law ( ), i $ r that is, [31], [32] automotive applications [133] [28], [29] [134] [135] Building climate control [27], [84] Microgrids [136] [ 105] networked control systems [137], [138] [139]…”

Section: General Formulation Of Smpcmentioning

confidence: 99%

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

2016

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Stochastic Model Predictive ControlM odel predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions { , , , } u u uN

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Stochastic predictive control for lane keeping assistance systems using a linear time-varying model

Cited by 22 publications

References 16 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

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