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Journal ArticleDOI

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

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TLDR
Critical review of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller’s performance are provided.
Abstract
Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

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Citations
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Towards connected autonomous driving: review of use-cases

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Path Following Control of Autonomous Ground Vehicle Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Control

TL;DR: A robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) and the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law.
References
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Journal ArticleDOI

Trajectory Tracking for Highly Automated Passenger Vehicles

TL;DR: In this paper, the longitudinal and lateral controller for dynamic trajectory tracking is presented to follow the planned trajectories generated by a co-pilot module in the safe way despite the presence of vehicle model uncertainties and also to guarantee a passenger comfort by generating soft actions on the steering wheel and accelerations.
Journal ArticleDOI

Fuzzy logic path tracking control for autonomous non-holonomic mobile robots: Design of System on a Chip

TL;DR: A System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core.
Journal ArticleDOI

Design of Lane Keeping Algorithm of Autonomous Vehicle

TL;DR: The paper presents the design and realization of lane keeping function of an autonomous electric go-cart, based on information from an optical camera with lane detection capabilities, which was designed and tuned in a Hardware-In-The-Loop framework.
Journal ArticleDOI

Sliding‐mode control for automated lane guidance of heavy vehicle

TL;DR: In this article, an active steering assistance system for heavy vehicles is developed to prevent lane departure and let the vehicle follow the road's center line, which is based on the super-twisting algorithm.
Journal ArticleDOI

Control and perception components for autonomous vehicle guidance. Application to the ROMEO vehicles

TL;DR: In this paper, the authors present several control and perception components that implement navigation behaviours in autonomous vehicles, and a behaviour-based control architecture integrating these components is also presented, focusing on autonomous tracking behaviours.
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