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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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The Architectural Implications of Autonomous Driving: Constraints and Acceleration

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Research Advances and Challenges of Autonomous and Connected Ground Vehicles

TL;DR: A representative architecture of CAVs is introduced and the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAV are surveyed and their significant research issues enumerated.
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Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects

TL;DR: The potential of cooperative information sharing for aiding autonomous high-speed overtaking manoeuvre is identified as a possible solution and shows that while advanced control methods improve tracking performance, in most cases the results are valid only within well-regulated conditions.
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Towards connected autonomous driving: review of use-cases

TL;DR: Although connectivity can enhance the performance of autonomous vehicles and contribute to the improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, traffic scenarios and the way of augmenting off-board information into vehicle control systems.
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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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Proceedings ArticleDOI

Modelling and trajectory following of an armoured vehicle

TL;DR: In this paper, a trajectory following strategy consisting of two feedback loops is employed on an armoured vehicle to follow a pre-defined trajectory using PID controllers using a 7 Degree-of-Freedoms (DOF) mathematical model.
Proceedings ArticleDOI

Hardware-in-the-loop simulation of automatic steering control: Outer-loop and inner-loop control design

TL;DR: A 9-degree of freedom (DOF) vehicle model combined with a closed loop driver model for the purpose of developing vehicle lateral control and the proposed SMAS system is capable of tracking the desired steering angle position and producing the front wheel steer angle for the use of vehicle model.
ReportDOI

Fuzzy Control for Autonomous Ground Vehicles

TL;DR: This paper describes a control technique, which uses this generic message, for navigating various autonomous ground vehicles, and uses two fuzzy model reference learning controllers (FMRLCs).
Proceedings ArticleDOI

Experimental comparison of adaptive controllers for trajectory tracking in agricultural robotics

TL;DR: In this paper, a simple kinematic controller has been implemented to calculate desired velocities based on current position and trajectory, i.e. the dynamics of the system, the output of this controller was used as input to a dynamic controller derived from a nonlinear model.

Techniques and Algorithms for Autonomous Vehicles in Forest Environment

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