Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges
01 May 2017-Journal of Intelligent and Robotic Systems (Springer Netherlands)-Vol. 86, Iss: 2, pp 225-254
TL;DR: 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.
Citations
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19 Mar 2018
TL;DR: With accelerator-based designs, this work is able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.
Abstract: Autonomous driving systems have attracted a significant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and finishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we first present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-offs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multicore CPUs are incapable of processing under the identified design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs, and ASICs, which can reduce the tail latency of the system by 169x, 10x, and 93x respectively. With accelerator-based designs, we are able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.
336 citations
Cites background from "Modelling and Control Strategies in..."
...present a review of the stateof-the-art algorithmic components used for path tracking in autonomous driving systems [2]....
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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.
Abstract: Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.
161 citations
Cites background from "Modelling and Control Strategies in..."
...speed, due to ignoring vehicle velocity and acceleration [52]....
[...]
...1) Geometric Vehicle Model: Geometric vehicle model exploits the geometric relationship in the vehicle, including the dimension and position between vehicle and path, without considering the velocity and acceleration of vehicle [52], [412]....
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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.
146 citations
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.
Abstract: Connected autonomous vehicles are considered as mitigators
of issues such as traffic congestion, road safety, inefficient fuel
consumption and pollutant emissions that current road transportation
system suffers from. Connected autonomous vehicles
utilise communication systems to enhance the performance of
autonomous vehicles and consequently improve transportation by
enabling cooperative functionalities, namely, cooperative sensing
and cooperative manoeuvring. The former refers to the ability to
share and fuse information gathered from vehicle sensors and road
infrastructures to create a better understanding of the surrounding
environment while the latter enables groups of vehicles to drive in
a co-ordinated way which ultimately results in a safer and more efficient
driving environment. However, there is a gap in understanding
howand to what extent connectivity can contribute to improving the
efficiency, safety and performance of autonomous vehicles. Therefore,
the aim of this paper is to investigate the potential benefits
that can be achieved from connected autonomous vehicles through
analysing five use-cases: (i) vehicle platooning, (ii) lane changing,
(iii) intersection management, (iv) energy management and (v) road
friction estimation. The current paper highlights that 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.
120 citations
Cites background from "Modelling and Control Strategies in..."
...5 g) and low vehicle side-slip angles (≤ 5◦) the tyres remain within the linear region of operation and hence, a dynamic bicycle model (linear) is sufficient to capture the relevant dynamics of a vehicle [81,83]....
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...investigation of controller properties [81]....
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...A comprehensive review of trajectory tracking control on the aspects of choice of vehicle model, control strategies, and controller performance criteria has been performed in [81]....
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...Dynamic vehicle models (full vehicle model, half vehicle model and bicycle model) attempt to address these issues by incorporating additional states such as (i) vehicle side-slip angle (β), (ii) tyre side-slip (α) and (iii) linear or non-linear tyre models and they were found to provide a more accurate representation of a vehicle during high-speed driving [81]....
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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.
Abstract: Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim–Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness.
109 citations
Cites methods from "Modelling and Control Strategies in..."
...Currently, there are mainly three types of vehicle model used in the path following controller development: geometric model, kinematic model and dynamic model [7], [8]....
[...]
...Among them, the geometric model is based on the Ackerman steering configurations, the kinematic vehicle model describes the motion of the vehicle, and the dynamic model describes not only the motion of the vehicle but also the internal states of the vehicle [7], [8]....
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...The Pure Pursuit is the most popular geometric controller which used the geometric model [7]–[9], it was reported being used by many...
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