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.read more
Citations
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Journal ArticleDOI
Deep reinforcement learning based path tracking controller for autonomous vehicle
I-Ming Chen,Ching-Yao Chan +1 more
TL;DR: The potential of using deep reinforcement learning (DRL) for vehicle control and applies it to the path tracking task is explored and the results show that the pathtracking capability in a low-speed driving condition is significantly enhanced.
Journal ArticleDOI
Object Recognition Based Interpolation With 3D LIDAR and Vision for Autonomous Driving of an Intelligent Vehicle
TL;DR: To process the acquired object data efficiently, the study devised an active-region-of-interest technique to ensure a fast processing speed while maintaining a high detection rate.
Journal ArticleDOI
Autonomous Vehicles in 5G and Beyond: A Survey
Saqib Hakak,Thippa Reddy Gadekallu,Swarna Priya Ramu,M. Parimala,Praveen Kumar Reddy Maddikunta,Chamitha de Alwis,Madhusanka Liyanage +6 more
TL;DR: The paper provides a comprehensive survey of recent developments in terms of standardisation activities on 5G autonomous vehicle technology and current projects, focusing on the emerging techniques required for integrating 5G technology with AVs, impact of 5G and B5G technologies on AVs along with security concerns in AVs.
Journal ArticleDOI
Autonomous road vehicles: recent issues and expectations
TL;DR: In this article, the authors present the current state of the art and prospects of automated vehicles (AVs) from various perspectives, focusing on revision of critical technologies, estimation of the impact on social aspects, identification of legal issues, consideration of factors in commercial success through user acceptance, and foresight carried out by other researchers.
Journal ArticleDOI
Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control
TL;DR: Co-simulations under several typical scenarios between MATLAB/Simulink and CarSim are conducted, and the results demonstrate excellent performance of the proposed mixed framework in both planning and tracking and also its real-time implementation.
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Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.
Proceedings ArticleDOI
A stable tracking control method for an autonomous mobile robot
TL;DR: The control rule and limiting method proposed are robot independent and hence can be applied to various kinds of mobile robots with a dead reckoning ability and was implemented on the autonomous mobile robot Yamabico-11.
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TL;DR: In this paper, the authors propose energy-based methods for stabilizing nonholonomic systems using non-holonomic control theory based on geometric properties of the system's properties. But they do not discuss the energy-independent methods of stabilisation.
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Predictive Active Steering Control for Autonomous Vehicle Systems
TL;DR: The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads, and two approaches with different computational complexities are presented.
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