Longitudinal Control of Autonomous Vehicles Consisting Power-Train With Non-Linear Characteristics

Aziziaghdam, Elif Toy; Alankus, Orhan Behic

doi:10.1109/tiv.2021.3115265

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…The vehicle control system is composed of longitudinal and lateral controllers as in [53, 54] respectively; or it can have a coupled controller for both of them [55] as it is described in Figure 4. Most of the control strategies proposed in the literature deal independently with longitudinal control or lateral control.…”

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

“…Several element reasons highlight the significance of the control phase in autonomous driving such as safety, trajectory tracking, manoeuvre execution, smooth ride, energy efficiency which includes optimal control strategies to improve energy efficiency by optimizing acceleration, the integration with decision making that determines high‐level actions while the control system translates these decisions into physical vehicle/robot movements, and finally, the control phase requires rigorous testing, validation, and verification to ensure its reliability and performance across a wide range of scenarios. Thorough testing helps identify potential issues and refine control algorithms [46–66].…”

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

“…There are two ways to design controllers for autonomous driving; either based on the imitation of the human driver, which includes approaches based on the use of driver models such as Artificial Intelligence 'AI', or the use of approaches based on the theory command. The vehicle control system is composed of longitudinal and lateral controllers as in [53,54] respectively; or it can have a coupled controller for both of them [55] as it is described in Figure 4. Most of the control strategies proposed in the literature deal independently with longitudinal control or lateral control.…”

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

See 2 more Smart Citations

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: Vehicle Control For Path Trackingmentioning

confidence: 99%

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

Section: Vehicle Control For Path Trackingmentioning

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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“…In [62], a novel longitudinal control method for autonomous vehicles was presented. The approach utilizes the reverse plant model of the vehicle to address non-linearity in the powertrain at low speeds and rapid acceleration/deceleration caused by surrounding vehicles.…”

Section: Longitudinal Control Of Autonomous Vehicles 221 Longitudinal...mentioning

confidence: 99%

Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey

Rizk,

Chaibet,

Kribèche

2023

Applied Sciences

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Autonomous driving has the potential to revolutionize mobility and transportation by reducing road accidents, alleviating traffic congestion, and mitigating air pollution. This transformation can result in energy efficiency, enhanced convenience, and increased productivity, as valuable driving time can be repurposed for other activities. The main objective of this paper is to provide a comprehensive technical survey of the latest research in the field of lateral, longitudinal, and integrated control techniques for autonomous vehicles. The survey aims to explore a wide range of techniques and methodologies employed to achieve precise steering control while also considering longitudinal aspects. Model-based control techniques form the foundation for control, utilizing mathematical models of vehicle dynamics to design controllers that effectively track desired speeds and/or steering behavior. Unlike model-free control techniques such as reinforcement learning and deep learning algorithms facilitate the integration of longitudinal and lateral control by learning control policies directly from data and without explicit knowledge of the underlying dynamics. Through this survey, the paper delves into the strengths, limitations, and advancements in both model-based and model-free control approaches for autonomous vehicles. It investigates their performance in real-world scenarios and addresses the technical challenges associated with their implementation. These challenges may include uncertainties in the environment, adaptability to dynamic conditions, robustness, safety considerations, and computational complexity.

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“…As technology continues to advance, numerous studies have demonstrated that modern data-based control methods can more precisely regulate vehicle speed, thereby enhancing the safety of driving. Aziziaghdam [11] devised a longitudinal controller consisting of two parts. The external controller determines the target speed, whereas the internal controller is responsible for determining the throttle and brake.…”

Section: Introductionmentioning

confidence: 99%

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

Zhou,

Pan,

Guan

et al. 2023

Applied Sciences

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Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver’s operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel.

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Longitudinal Control of Autonomous Vehicles Consisting Power-Train With Non-Linear Characteristics

Cited by 18 publications

References 29 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

Model-Based Control and Model-Free Control Techniques for Autonomous Vehicles: A Technical Survey

A Novel Longitudinal Control Method Integrating Driving Style and Slope Prediction for High-Efficiency HD Vehicles

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