A Review of some Pure-Pursuit based Path Tracking Techniques for Control of Autonomous Vehicle

Samuel, Moveh; Hussein, Mohamed E.; Mohamad, Maziah

doi:10.5120/ijca2016908314

Cited by 124 publications

(59 citation statements)

References 28 publications

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“…Considering the factors that influence the control strategy most in the model [70], the main uncertainty factors lie in the wheelbase and steering ratio. Furthermore, the typical pure pursuit algorithm [40][41][42] and classical second-order LADRC method (SO-LADRC) [70][71][72] were adopted as comparison algorithms. Table 1 lists the key parameters for the autonomous road sweeper and the controller.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, as traditional PID fails to ensure lateral control stability due to the inflexible parameter settings, BP-PID (back-propagation proportion-integration-differentiation) was designed by Han et al [39]. As a prevalent variant of PID, pure pursuit has proven to be an indispensable method for vehicle control owing to its simple implementation and satisfactory performance [16,40]. Snider et al [41] conducted pure pursuit experiments on the lane change course, which shows that a short look-ahead distance provides more accurate tracking, while a longer distance provides smoother tracking.…”

Section: Related Workmentioning

confidence: 99%

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HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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show abstract

“…As described in [91], in its basic implementation, the Pure Pursuit algorithm relies on the following procedure.…”

Section: Geometric Path Tracking Methodsmentioning

confidence: 99%

Trends in vehicle motion control for automated driving on public roads

Klomp

Jonasson

Laine

et al. 2019

Vehicle System Dynamics

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In this paper we describe how vehicle systems and the vehicle motion control are affected by automated driving on public roads. We describe the redundancy needed for a road vehicle to meet certain safety goals. The concept of system safety as well as system solutions to fault tolerant actuation of steering and braking and the associated fault tolerant power supply is described. Notably restriction of the operational domain in case of reduced capability of the driving automation system is discussed. Further we consider path tracking, state estimation of vehicle motion control required for automated driving as well as an example of a minimum risk maneuver and redundant steering by means of differential braking. The steering by differential braking could offer heterogeneous or dissimilar redundancy that complements the redundancy of described fault tolerant steering systems for driving automation equipped vehicles. Finally the important topic of verification of driving automation systems is addressed.

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“…In recent years, much research work on trajectory tracking have been studied [8][9][10][11][12][13] and the main challenges of achieving accurate trajectory tracking lie in the following aspects: (1) non-holonomic property and multi-constrains of AGVs; (2) trade-off between vehicle model accuracy and computation efficiency; (3) uncertainties and time-varying parameters of vehicle dynamic model; (4) external disturbances of complex driving environment. Besides, the interaction between road and tires is also an important source of coupling.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

Yang

Qin

Huang

et al. 2020

Preprint

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A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.

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A Review of some Pure-Pursuit based Path Tracking Techniques for Control of Autonomous Vehicle

Cited by 124 publications

References 28 publications

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Trends in vehicle motion control for automated driving on public roads

Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

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