Abstract

Abstract. Air pollution, energy consumption, and human safety issues have aroused people's concern around the world. This phenomenon could be significantly alleviated with the development of automatic driving techniques, artificial intelligence, and computer science. Autonomous vehicles can be generally modularized as environment perception, path planning, and trajectory tracking. Trajectory tracking is a fundamental part of autonomous vehicles which controls the autonomous vehicles effectively and stably to track the reference trajectory that is predetermined by the path planning module. In this paper, a review of the state-of-the-art trajectory tracking of autonomous vehicles is presented. Both the trajectory tracking methods and the most commonly used trajectory tracking controllers of autonomous vehicles, besides state-of-art research studies of these controllers, are described.

Highlights

  • An autonomous vehicle is a motor vehicle that uses artificial intelligence, sensors, and global positioning system coordinates to drive itself without the active intervention of a human operator (Anagnostopoulos, 2012)

  • According to the latest survey, by 2040, the proportion of people traveling by autonomous taxi and public transport in Germany will have increased from 20.0 % to about 32 % (Kaltenhäuser et al, 2020), which means autonomous vehicles will play an essential role in public transport

  • This paper presents a review of the current development of the trajectory tracking of autonomous vehicles

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Summary

Introduction

An autonomous vehicle is a motor vehicle that uses artificial intelligence, sensors, and global positioning system coordinates to drive itself without the active intervention of a human operator (Anagnostopoulos, 2012). Most commercial vehicles can only achieve level 1 to level 2 autonomy due to the available sensor constraints and high costs. According to the latest survey, by 2040, the proportion of people traveling by autonomous taxi and public transport in Germany will have increased from 20.0 % to about 32 % (Kaltenhäuser et al, 2020), which means autonomous vehicles will play an essential role in public transport This is in line with the concept of energy saving and global green environmental protection.

Geometric methods
Pure pursuit method
Stanley method
Model-based tracking methods
Kinematic-model-based methods
Dynamic-model-based methods
Trajectory tracking controller
Fuzzy-logic-based controller
Sliding mode control methods
Model predictive control methods
Immersion and invariance
Findings
Conclusion and development trend
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