A Review of Autonomous Vehicle
Path Tracking Algorithm Research
Yaoting Chen, Yanping Zheng *
College of Automobile and Traffic Engineering, Nanjing Forestry
University, Nanjing 210037, China;
carmelo@njfu.edu.cn (Y.C.); qq1137656476@163.com(Z.P.);
* Correspondence: zhengyp@njfu.com.cn; Tel.: +86-138-5186-4173
Abstract : Driverless technology aims to improve driving safety,
accuracy and comfort. Path tracking is a basic component of the motion
control module of autonomous vehicles, and its control algorithm
directly affects the path tracking effect. Based on the preliminary
results of the application of path tracking control algorithm, this
paper analyzes the principles, advantages and disadvantages,
applications and current research progress of the path tracking
algorithm under different working conditions from the perspective of
different working conditions at low speed and high speed, and provides
an outlook on the future development, aiming to provide reference for
future in-depth research.Keywords : Autonomous vehicles; Motion control; Path tracking;
Pateral control
Introduction
In recent years, autonomous vehicle have become increasingly popular in
the automotive markets, which mainly consist of task decision,
environment sensing, path planning, path tracking and vehicle control
subsystems [1,2]. As a crucial part of the architecture of
driverless vehicles, path tracking control is undoubtedly a key research
focus for scholars from various countries. The role of path tracking
control is to control the actuator action based on the vehicle state
information given by each on-board sensor and the planned reference path
to ensure that the vehicle travels along the planned path, and to
control the actuator in time to reduce the deviation between the vehicle
position and the planned path when the vehicle deviates from the planned
path [3]. Therefore, the accuracy of path tracking control directly
determines whether the driverless vehicle can follow the planned
trajectory, which is of great significance for traffic safety. Many
research results have been generated around path tracking algorithms in
recent years, and there are common problems and solutions in solving the
problem of reducing tracking errors.
In this paper, the path tracking control algorithms are divided into two
categories for low-speed and high-speed conditions according to the
speed of the car. Low-speed conditions mainly refer to the driving
environment where the vehicle speed is lower than 30km/h, including
scenarios such as factories, ports, campuses, farms, mines, and parking
lots [4,5]. Low-speed driverless cars do not need to consider the
influence of vehicle dynamics characteristics due to low vehicle speed,
and only need to control the accuracy of path tracking, which requires
high control accuracy [6-8]. High-speed conditions mainly refer to
the driving environment where the vehicle speed is higher than 30 km/h.
Due to the high speed and the existence of high-speed lane changing and
high-speed cornering conditions, the influence of vehicle dynamics on
the maneuvering stability needs to be considered when path tracking
control is carried out, and the control effect directly affects the
driving safety of the vehicle [9].
Although the same path tracking control algorithm can be used for
driverless vehicles under both low-speed and high-speed conditions, the
path tracking control faces different challenges and the controller
design differs due to the difference in control objectives. Under
low-speed conditions, considering the motion characteristics of the
vehicle, the path curvature of the vehicle driving is larger and the
vehicle heading angle changes more, which makes the optimal solution of
the nonlinear problem more difficult and leads to the problem of low
path tracking accuracy [10]. Under high-speed conditions, due to the
contradiction between the complexity of the vehicle dynamics model and
the computational real-time, driverless vehicles in high-speed
situations need a longer prediction time domain for hazard avoidance,
further increasing the difficulty of vehicle controller design
[11,12]. Therefore, in this paper, the problems faced by path
tracking control and their algorithms are sorted out according to two
operating conditions, low-speed and high-speed, respectively, and the
characteristics of different algorithms are analyzed with the aim of
providing references for future in-depth research.
2. Research status of low-speed path tracking algorithm
The kinematic characteristics of the vehicle are more influential when
the driverless vehicle is at low speed, and due to its own small lateral
acceleration and the existence of the minimum turning radius constraint,
the influence of vehicle dynamics is generally not considered, i.e.,
there is no need to consider the maneuvering stability of the vehicle,
and the kinematic model of the vehicle can be directly used as the
control model of the path tracking algorithm, at which time the path
tracking controller designed based on the kinematic model has reliable
control performance [ 13-16]. At present, the path tracking
algorithms that are more applied in low-speed conditions are PID control
method (Process Identifier Derivative, PID), pure tracking control
algorithm (Pure Pursuit, PP), Stanley algorithm, model predictive
control (Model Predictive Control, MPC), etc. [ 17-19].
2.1 PID algorithm
The PID control method is a widely used automatic controller that
controls by proportional (P), integral (I), and differential (D) of
deviation, which has the advantages of simple principle, easy
implementation, wide applicability, and relatively independent control
parameters [20,21]. Figure 1 and Figure 2 show the PID control
schematic and feedforward PID control framework, respectively, in which
the proportional link can reflect the state deviation of the controlled
object in a timely and proportional manner, the integral link improves
the non-differential degree of the controlled object through the memory
of the state deviation, the differential link can reflect the trend of
the state deviation to speed up the system action, and the feedforward
control can compensate the disturbance and weaken the effect of the
disturbance on the system output [22]. of the system [22].
Currently, many studies are based on PID algorithms or joint control
with PID for path tracking. For example, the literature [23,24]
designed a path tracking controller based on PID algorithm and verified
the advantages of this algorithm in low speed conditions. Based on the
idea of hierarchical control, the literature [25] designed the
method of joint control of PID and MPC and verified that the controller
has good tracking effect in low-speed conditions. Considering the
characteristics of low speed and small curvature working conditions,
literature [26] designed a transverse controller based on neural
network parallel PID, and used PID algorithm to adjust the error caused
by the neural network training to further improve the control accuracy.
For the problem of difficult adjustment of PID controller parameters,
literature [27] proposed a method based on the combination of PID
control and pre-scanning control, and designed a PID controller with
adaptive parameter values using fuzzy inference control.Mashadi B et al.
introduced a genetic algorithm to adjust the parameters and compared the
control effect of the traditional PID controller, proving that the
controller has better tracking accuracy, real-time performance and
vehicle driving stability [28].
PID control is simple and effective, but due to the delay between the
sensor and the actuator, the PID controller can only get the position
deviation of the previous moment, so there is an unavoidable delay in
the system, and this error is not negligible when the vehicle speed is
fast, so the PID algorithm is poorly adaptable and vulnerable to the
change of the road environment, and each parameter is difficult to
adjust and cannot meet the requirements of path tracking under
high-speed conditions, so In practical applications, it is generally
used in conjunction with other control algorithms.