The Study on Path Tracking Control Method Based on Fuzzy-CMAC for Autonomous Vehicle in Rural Environment

Chen, Jiajia; Han, Wei; Zhao, Pan; Wei, Zhenya; Liu, Xingtao

doi:10.1109/icspcc50002.2020.9259449

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The latter recommends optimal steering and velocity controls that are based on fuzzy rules inspired from human experience. Similarly, Chen et al [131] used CMAC neural network [132] with fuzzy logic to control an autonomous vehicle. The neural network ensures the self‐learning ability of the controller which eliminate errors, while the fuzzy logic module improves the control quality by suppressing disturbances and increasing robustness.…”

Section: Vehicle Controlmentioning

confidence: 99%

Lateral control for autonomous wheeled vehicles: A technical review

Kebbati

Ait-Oufroukh

Ichalal

et al. 2022

Asian Journal of Control

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Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments.

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Section: Vehicle Controlmentioning

confidence: 99%