Review of Control Strategies for Autonomous Vehicles

DOI

10.2991/978-94-6463-864-6_48How to use a DOI?

Keywords

Autonomous Driving; Pid Control; Model Predictive Control; Deep Learning; Reinforcement Learning

Abstract

With the rapid advancement of artificial intelligence, computer vision, and vehicle-to-everything (V2X) technologies, autonomous driving is increasingly becoming an integral part of future intelligent transportation systems. As the core mechanism ensuring safe and stable vehicle operation, control strategies directly affect trajectory tracking accuracy, dynamic response capabilities, and adaptability to environmental disturbances. This paper provides a comprehensive review of current research on control strategies for autonomous vehicles, categorizing them into four main groups: traditional PID control and its enhancements, adaptive and model predictive control, intelligent control methods based on deep learning and reinforcement learning, and hybrid and robust control strategies. From the perspectives of theoretical foundations, structural design, and representative applications, the advantages and limitations of each category are analyzed in depth. The study further highlights the emerging trend toward the integration of model-based and data-driven approaches, aiming to develop high-performance control systems that are adaptive, robust, and verifiable. This review offers both theoretical insights and practical references for future research and engineering applications in autonomous driving control.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zikuo Zhou
PY  - 2025
DA  - 2025/10/23
TI  - Review of Control Strategies for Autonomous Vehicles
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 530
EP  - 550
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_48
DO  - 10.2991/978-94-6463-864-6_48
ID  - Zhou2025
ER  -