<div>5. Summary and outlook</div><div>(1) Path tracking control is one of the research hotspots in the field
of unmanned vehicles, and the preliminary research is usually based on
the methods of vehicle kinematics and dynamics, using information such
as front wheel turning angle and vehicle position and heading angle
error and road curvature for path tracking control.</div><div>(2) In the research of path tracking control for low-speed conditions,
the more applied are PID, PP, Stanley and MPC algorithms. By improving
and using the related algorithms in combination, the influence of the
front wheel rotational speed constraint on the path tracking accuracy is
reduced. As the path tracking control in low-speed conditions requires
low real-time, the current research basically meets the needs of
low-speed path tracking control, and has more mature applications in
automatic parking and special vehicles.</div><div>(3) In the research of path tracking control in high-speed conditions,
the more applied are LQR, MPC, ADRC, H∞, SMC and RL algorithms. In order
to achieve the purpose of controlling the path tracking accuracy and
vehicle driving stability, and to solve the coupling relationship of
optimization objectives, the current research usually combines two or
more algorithms according to the actual needs to achieve complementary
advantages and disadvantages.</div><div>(4) MPC algorithm has become a mainstream method for path tracking
control of driverless vehicles at present, but its computational
real-time and ability to handle system uncertainty are still the future
development direction.</div><div>(5) Since the decision indexes of speed regulation and weight
distribution are usually related to environmental factors such as ground
adhesion coefficient and lateral wind speed, and there is less research
in this direction by scholars concerned, with the parallel development
of machine learning theory and unmanned vehicle computing power, its
application under extreme working conditions is also an important
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