A survey on the use of artificial intelligence in autonomous driving

Authors

  • Boyuan Kong

DOI:

https://doi.org/10.54097/v0gjay68

Keywords:

Autonomous driving, artificial intelligence, deep learning, reinforcement learning.

Abstract

Autonomous driving and artificial intelligence are the most popular research projects in the field of technology today. As the high technology, autonomous driving relies on perception, decision-making, and control systems, and the performance of these systems largely depends on the application of artificial intelligence nowadays. Fortunately, there are plenty of applications of artificial intelligence in several aspects of autonomous driving. This paper aims to introduce the relationship between autonomous driving and artificial intelligence by reviewing several literatures and analyzing the applications of deep learning (DL), reinforcement learning (RL), and graph neural networks (GNN) in autonomous driving.

Downloads

Download data is not yet available.

References

[1] Parekh, Darsh, et al. ‘A Review on Autonomous Vehicles: Progress, Methods and Challenges’. Electronics, vol. 11, no. 14, MDPI AG, July 2022, p. 2162. DOI: https://doi.org/10.3390/electronics11142162

[2] Ma, Yifang, et al. "Artificial intelligence applications in the development of autonomous vehicles: A survey." IEEE/CAA Journal of Automatica Sinica 7.2 (2020): 315-329. DOI: https://doi.org/10.1109/JAS.2020.1003021

[3] Grigorescu, Sorin, et al. "A survey of deep learning techniques for autonomous driving." Journal of field robotics 37.3 (2020): 362-386. DOI: https://doi.org/10.1002/rob.21918

[4] Niu, Zhaoyang, Guoqiang Zhong, and Hui Yu. "A review on the attention mechanism of deep learning." Neurocomputing 452 (2021): 48-62. DOI: https://doi.org/10.1016/j.neucom.2021.03.091

[5] Chib, Pranav Singh, and Pravendra Singh. "Recent advancements in end-to-end autonomous driving using deep learning: A survey." IEEE Transactions on Intelligent Vehicles (2023). DOI: https://doi.org/10.1109/TIV.2023.3318070

[6] Kiran, B. Ravi, et al. "Deep reinforcement learning for autonomous driving: A survey." IEEE Transactions on Intelligent Transportation Systems 23.6 (2021): 4909-4926. DOI: https://doi.org/10.1109/TITS.2021.3054625

[7] Chen, Guoxi, Ya Zhang, and Xinde Li. "Attention-based highway safety planner for autonomous driving via deep reinforcement learning." IEEE Transactions on Vehicular Technology (2023). DOI: https://doi.org/10.1109/TVT.2023.3304530

[8] Ge, Lun, et al. "Deep reinforcement learning navigation via decision transformer in autonomous driving." Frontiers in Neurorobotics 18 (2024): 1338189. DOI: https://doi.org/10.3389/fnbot.2024.1338189

[9] Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE transactions on neural networks and learning systems 32.1 (2020): 4-24. DOI: https://doi.org/10.1109/TNNLS.2020.2978386

[10] Xi, Zerong, and Gita Sukthankar. "A Graph Representation for Autonomous Driving." The 36th Conference on Neural Information Processing Systems Workshop. Vol. 7. No. 8. 2022.

[11] CAI, Peide, et al. "DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks." IEEE Transactions on Intelligent Transportation Systems 23.11 (2022): 21102-21112. DOI: https://doi.org/10.1109/TITS.2022.3184990

Downloads

Published

18-02-2025

How to Cite

Kong, B. (2025). A survey on the use of artificial intelligence in autonomous driving. Highlights in Science, Engineering and Technology, 124, 146-151. https://doi.org/10.54097/v0gjay68