Progress of Deep Reinforcement Learning in Autonomous Driving in the Past Three Years

Fujia Yu

doi:10.54254/2755-2721/2025.LD28603

Applied and Computational EngineeringOpen access

Progress of Deep Reinforcement Learning in Autonomous Driving in the Past Three Years

Research Article

Open Access

Progress of Deep Reinforcement Learning in Autonomous Driving in the Past Three Years

Fujia Yu ^1*

¹ 1School of Economics and Trade, Statistics major, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, China

^*Corresponding author: fujiayu@stepbystep.freeqiye.com

Published on 28 October 2025

ACE Vol.202

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-497-7

ISBN (Online): 978-1-80590-498-4

Download Cover

Abstract

Many challenges such as environmental complexity, decision-making security, and algorithm generalization ability remain as the main problems faced by autonomous driving technology. Current research focuses on multimodal perception, end-to-end control systems, and reinforcement learning frameworks, but still has problems such as insufficient handling of long-tail scenarios, weak interpretability of black-box decisions, and high training costs. This paper's research can be deepened in three directions: integrating large language models (LLMs) with visual foundation models (VLMs) to enhance the scene understanding and few-shot generalization ability of end-to-end systems through semantic reasoning; developing hybrid learning frameworks that combine imitation learning and model-based reinforcement learning (such as the DIRL method) to reduce the demand for high-risk interactions; and building high-fidelity simulation environments to generate dynamic scenes using multimodal trajectory prompts and optimize the robustness of algorithms in extreme conditions. This paper can solve the black-box problem of autonomous driving through decision transparency enhanced by LLMs; lightweight models and hybrid training strategies can significantly reduce computational costs; the simulation supplementation of long-tail scenarios by world models will promote the implementation of safety standards and provide technical support for the commercialization of fully autonomous driving.

Keywords:

Deep reinforcement learning, Autonomous driving systems, Large language models

View PDF

References

[1]. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., ... & Zieba, K. (2016). End to end learning for self-driving cars. arXiv preprint arXiv: 1604.07316.

[2]. Yang, Z., Jia, X., Li, H., & Yan, J. (2023). LLM4Drive: A survey of large language models for autonomous driving. arXiv preprint arXiv: 2311.01043.

[3]. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.

[4]. Cai, P., Wang, H., Huang, H., Liu, Y., & Liu, M. (2021). Vision-based autonomous car racing using deep imitative reinforcement learning. IEEE Robotics and Automation Letters, 6(4), 7262–7269.

[5]. Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., ... & Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11621–11631).

[6]. Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J., ... & Shah, A. (2019). Learning to drive in a day. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8248–8254). IEEE.

[7]. Pan, F., & Bao, H. (2021). Research progress on autonomous driving control technology based on reinforcement learning. Journal of Image and Graphics, (1).

[8]. Chen, X., Peng, M., Tiu, P., Wu, Y., Chen, J., Zhu, M., & Zheng, X. (2024). GenFollower: Enhancing car-following prediction with large language models. IEEE Transactions on Intelligent Vehicles.

[9]. Mengjie, W., Huiping, Z., Jian, L., Wenxiu, S., & Song, Z. (2025). Research on driving scenario technology based on multimodal large language model optimization. arXiv preprint arXiv: 2506.02014.

[10]. Li, X., Wu, C., Yang, Z., Xu, Z., Liang, D., Zhang, Y., ... & Wang, J. (2025). DriVerse: Navigation world model for driving simulation via multimodal trajectory prompting and motion alignment. arXiv preprint arXiv: 2504.18576.

[11]. Kim, H., & Kee, S. C. (2023). Neural network approach super-twisting sliding mode control for path-tracking of autonomous vehicles. Electronics, 12(17), 3635.

References

[1]. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., ... & Zieba, K. (2016). End to end learning for self-driving cars. arXiv preprint arXiv: 1604.07316.

[2]. Yang, Z., Jia, X., Li, H., & Yan, J. (2023). LLM4Drive: A survey of large language models for autonomous driving. arXiv preprint arXiv: 2311.01043.

[4]. Cai, P., Wang, H., Huang, H., Liu, Y., & Liu, M. (2021). Vision-based autonomous car racing using deep imitative reinforcement learning. IEEE Robotics and Automation Letters, 6(4), 7262–7269.

[7]. Pan, F., & Bao, H. (2021). Research progress on autonomous driving control technology based on reinforcement learning. Journal of Image and Graphics, (1).

[8]. Chen, X., Peng, M., Tiu, P., Wu, Y., Chen, J., Zhu, M., & Zheng, X. (2024). GenFollower: Enhancing car-following prediction with large language models. IEEE Transactions on Intelligent Vehicles.

[11]. Kim, H., & Kee, S. C. (2023). Neural network approach super-twisting sliding mode control for path-tracking of autonomous vehicles. Electronics, 12(17), 3635.

Cite this article

Yu,F. (2025). Progress of Deep Reinforcement Learning in Autonomous Driving in the Past Three Years. Applied and Computational Engineering,202,15-22.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.202

ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).