Analysis of Autonomous Driving Control Strategies Based on Deep Learning

DOI

10.2991/978-94-6239-648-7_24How to use a DOI?

Keywords

Autonomous driving; control strategies; deep learning

Abstract

As a core component of intelligent transportation systems, the development of autonomous driving technology is of milestone significance for enhancing traffic safety, optimizing traffic efficiency, and improving travel experience. Control strategies, as a key execution link in the “perception-decision-control” closed loop of autonomous driving systems, directly determine the safety, comfort, and robustness of vehicle dynamic responses. Traditional control methods such as PID control and model predictive control struggle to cope with dynamic road conditions and multi-objective coordination requirements in complex traffic scenarios like urban multi-interaction environments and extreme weather. In contrast, deep learning, with its powerful data-driven modeling capabilities, ability to capture temporal dependencies, and multi-modal information fusion capacity, provides new pathways for optimizing autonomous driving control strategies. In recent years, autonomous driving technology has made a series of advancements in optimizing control strategies, but it also faces many challenges, such as model generalization capability, interpretability, and real-time performance. This article aims to organize the current research status, analyze existing issues, and look forward to future development trends in conjunction with the latest research results, providing references to promote further and more practical development of autonomous driving technology.

Copyright

© 2026 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  - Yumin Cai
PY  - 2026
DA  - 2026/04/24
TI  - Analysis of Autonomous Driving Control Strategies Based on Deep Learning
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 215
EP  - 223
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_24
DO  - 10.2991/978-94-6239-648-7_24
ID  - Cai2026
ER  -