Fuzzy logic controller for autonomous vehicle path tracking

Allou, Sami; Zennir, Youcef; Belmeguenaï, Aïssa

doi:10.1109/sta.2017.8314969

Cited by 17 publications

(11 citation statements)

References 5 publications

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“…Auday Al-Mayyahi et al [18] proposed a fractional-order PID path tracking control strategy to obtain the heading angle and speed control laws, and the controller parameters are adjusted through particle swarm optimization algorithm. Some controller are designed using fuzzy PID [19][20]. Muhammad Aizzat Zakaria et al studied the adaptive PID control strategy by adaptive road curvature observation [22].…”

Section: B Pid Control Methodsmentioning

confidence: 99%

“…When the operating conditions change greatly, the control parameters are no longer optimal. For this reason, some scholars have proposed adaptive PID control methods [19][20]22]. However, adaptive parameters tuning are more difficult.…”

Section: B Pid Control Methodsmentioning

confidence: 99%

“…[ [16][17][18][19][20][21][22] Model-Free Control preview course deviation It has a simple controller structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

106

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: B Pid Control Methodsmentioning

confidence: 99%

Section: B Pid Control Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

106

View full text Add to dashboard Cite

show abstract

“…It illustrates that the fuzzy logic controller can be modularized as fuzzification, knowledge base, fuzzy inference, and defuzzification. The function of each module of the fuzzy logic controller refers to Allou et al (2017). However, sharing the same structure is not equivalent to having the same design rules.…”

Section: Fuzzy-logic-based Controllermentioning

confidence: 99%

Review article: State-of-the-art trajectory tracking of autonomous vehicles

Zhang

2021

Mech. Sci.

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Abstract. Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.

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“…A human driver immersed into a virtual-reality scenario acts on a virtual vehicle, enabling the assessment both subjectively and objectively of the influence of multiple design choices related, but not limited to, VD performances. Furthermore, FVMs are also enabling the development of more recent control strategies aiming for autonomous driving, such as fuzzy controllers [35] and/or proportional-integral-derivative (PID) controllers [36,37]. This work's contribution builds on the verge of extending the above-mentioned DiL approach, including some of the more basic autonomous driving functions, such as steering and throttle control, leading to the concept of virtual-driver-in-the-loop (vDiL) simulations.…”

Section: Introductionmentioning

confidence: 99%

On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

et al. 2021

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Assessing passenger cars’ dynamic performance is a critical aspect for car industries, due to its impact on the overall vehicle safety evaluation and the subjective nature of the involved handling and comfort metrics. Accordingly, ISO standards, such as ISO 4138 and ISO 3888, define several specific driving tests to assess vehicle dynamics performance objectively. Consequently, proper evaluation of the dynamic behaviour requires measuring several physical quantities, including accelerations, speed, and linear and angular displacements obtained after instrumenting a vehicle with multiple sensors. This experimental activity is highly demanding in terms of hardware costs, and it is also significantly time-consuming. Several approaches can be considered for reducing vehicle development time. In particular, simulation software can be exploited to predict the approximate behaviour of a vehicle using virtual scenarios. Moreover, motion platforms and detail-scalable numerical vehicle models are widely implemented for the purpose. This paper focuses on a customized simulation environment developed in C++, which exploits the advantages of object-oriented programming. The presented framework strives to perform concurrent simulations of vehicles with different characteristics such as mass, tyres, engine, suspension, and transmission systems. Within the proposed simulation framework, we adopted a hierarchical and modular representation. Vehicles are modelled by a 14 degree-of-freedom (DOF) full-vehicle model, capable of capturing the dynamics and complemented by a set of scalable-detail models for the remaining sub-systems such as tyre, engine, and steering system. Furthermore, this paper proposes the usage of autonomous virtual drivers for a more objective evaluation of vehicle dynamic performances. Moreover, to further evaluate our simulator architecture’s efficiency and assess the achieved level of concurrency, we designed a benchmark able to analyse the scaling of the performances with respect to the number of different vehicles during the same simulation. Finally, the paper reports the proposed simulation environment’s scalability resulting from a set of different and varying driving scenarios.

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Fuzzy logic controller for autonomous vehicle path tracking

Cited by 17 publications

References 5 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Review article: State-of-the-art trajectory tracking of autonomous vehicles

On the Benefits of Using Object-Oriented Programming for the Objective Evaluation of Vehicle Dynamic Performance in Concurrent Simulations

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