Model predictive path tracking control for automated road vehicles: A review

Stano, P.; Montanaro, Umberto; Tavernini, Davide; Tufo, Manuela; Fiengo, Giovanni; Novella, L.; Sorniotti, Aldo

doi:10.1016/j.arcontrol.2022.11.001

Cited by 44 publications

(34 citation statements)

References 157 publications

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“…Model Predictive Control (MPC) method is extensively employed in the field of autonomous vehicles navigation for path planning and control as investigated in details attested in [17]; due to its capability of controlling multi-parameters and considering states and control actions by anticipating the future behaviour of the AV using a mathematical model of the AV dynamics. This method is originally used as a control method as developed in Section 3, and has been extended in the literature for decision-making applications as reported in [18].…”

Section: Traditional Methodsmentioning

confidence: 99%

“…The integration of MPC for the decision-making process enables autonomous systems to not only follow desired trajectories but also make intelligent decisions to optimize performance, safety, and efficiency. The literature on MPC for decisionmaking covers a range of topics including the formulation of decision problems, optimization techniques, trade-offs between conflicting objectives, and real-time implementation considerations [17][18][19]. according to the broad literature on MPC presented in [17], MPC is the most widely applied approach for path tracking control of AVs, appearing in 50% of papers on the subject between 2019 and 2021, and it also used in decision-making.…”

Section: Traditional Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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This abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

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Section: Traditional Methodsmentioning

confidence: 99%

Section: Traditional Methodsmentioning

confidence: 99%

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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“…Literature reviews published in recent years have explored ICC architectures for conventional vehicles [ 1 , 97 , 98 , 99 , 100 ]. Simultaneously, another group of review papers has examined PT tasks specifically in the context of AVs [ 101 , 102 , 103 , 104 , 105 ]. In both research and review scientific publications related to ICC and PT, these problems are investigated separately.…”

Section: Previous Studiesmentioning

confidence: 99%

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Skrickij,

Kojis,

Šabanovič

et al. 2024

Sensors

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Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

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“…Achieving a delicate balance between responsiveness, accuracy and computational efficiency is crucial to ensure real-time adaptability in dynamic driving environments. As the demand for autonomous vehicles continues to increase, it is crucial to refine trajectory tracking control algorithms to effectively handle the complexity of various road scenarios [6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control

Chen,

Lai,

et al. 2024

Preprint

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The prediction horizon is a key parameter in model predictive control (MPC), related to the effectiveness and stability of model predictive control. In vehicle control, the selection of prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the Proximal Policy Optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. In this paper, we constructed the vehicle dynamics model and designed a basic model prediction control. We have established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared the MPC of the fixed Prediction Horizon. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong trajectory tracking capability.

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Model predictive path tracking control for automated road vehicles: A review

Cited by 44 publications

References 157 publications

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Prediction Horizon-varying Model Predictive Control (MPC)for Autonomous Vehicle Control

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