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Review
. 2023 Jul 4;23(13):6140.
doi: 10.3390/s23136140.

A Review of the Motion Planning and Control Methods for Automated Vehicles

Xiaohua Song  1 , Huihui Gao  1 , Tian Ding  1 , Yunfeng Gu  1 , Jing Liu  2 , Kun Tian  1
Affiliations
Review

A Review of the Motion Planning and Control Methods for Automated Vehicles

Xiaohua Song et al. Sensors (Basel). .

Abstract

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other's strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated 'human-vehicle-road' traffic systems.

Keywords: automated vehicles; motion planning; tracking control.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A flowchart of the A* algorithm.
Figure 2
Figure 2
Two-part trajectory planning framework.
Figure 3
Figure 3
Dynamic window.
Figure 4
Figure 4
The concept of the artificial potential field method.
Figure 5
Figure 5
The block diagram of the single neuron-adaptive PID controller.
Figure 6
Figure 6
The vehicle control system architecture.
Figure 7
Figure 7
The algorithm process.
Figure 8
Figure 8
The diagram of pure pursuit control.
Figure 9
Figure 9
The structure of the controller.
Figure 10
Figure 10
The model predictive control.
See this image and copyright information in PMC

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