Table 4 Summary of path tracking control methods [125]

Pure Pursuit & Stanley

Position deviation & course deviation

Simple layout, suitable for vehicle position control

Difficult to apply to high speed and large road curvature conditions

[126,127,128]

PID

Position deviation & course deviation

Simple, easy to apply

Poor versatility, difficult in tuning control parameters

[129,130,131,132]

Model-Free Control 

Preview course deviation

Simple controller structure

Stability analysis is more difficult

[133,134,135]

LQR

System states & control input

Easy to achieve closed-loop optimal control objective

Controller design based on linear model (poor robustness)

[136,137,138]

Feedforward and Feedback

Feedback error, feedforward information

Able to deal with external disturbances, modeling errors, and sensor noise

Require more expensive sensors

[139, 140]

MPC

System states & control input

Able to handle system constraints and future prediction in design process

Difficult to analyze system stability, has high computational cost

[141,142,143]

H_∞Control

System H_∞ performance index

Easy to establish H_∞constraints, strong robustness

Has complex solution process and theoretical derivation, can only handle bounded disturbances

[144,145,146]

Sliding Mode Control

Position deviation & course deviation

Fast response and insensitivity to parameter changes and disturbances

Chattering effect that requires adaptive mechanism

[147,148,149]

Robust MPC

System states & control input

Able to handle system constraints and has strong robustness

Difficult to analyze system stability and has high computational cost

[150,151,152]

Neural Network-based Observation

N/a

Optimal approximation, rapid training, fast convergence

Require large amount of vehicle state information for training

[153,154,155]