Study of Different Module Technologies for Autonomous Driving
- DOI
- 10.2991/978-94-6463-823-3_49How to use a DOI?
- Keywords
- Autonomous Driving; Deep Learning; Computer Vision
- Abstract
As vehicles become popular, there are high requirements for the safety and convenience of vehicles and traffic, which gives rise to the birth and development of autonomous driving technology. However, the current autonomous driving technology is not particularly perfect, and there are still many problems to be faced. In this paper, the autonomous driving technology is divided into different modules (perception, localization, decision making, and control), and the relevant theoretical basis is introduced. At the same time, some current applications and previous research results are listed. It is found that autonomous driving technology relies on artificial intelligence technologies such as computer vision, deep learning frameworks, hardware support such as real-time positioning technology and sensors, as well as mathematical models such as Markov decision processes and game Theory. Autonomous driving has made great progress with the support of the above technologies, but there are still some technical and testing, and verification problems to be overcome.
- 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.
Cite this article
TY - CONF AU - Shuaiyu Chen PY - 2025 DA - 2025/08/31 TI - Study of Different Module Technologies for Autonomous Driving BT - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025) PB - Atlantis Press SP - 492 EP - 499 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-823-3_49 DO - 10.2991/978-94-6463-823-3_49 ID - Chen2025 ER -