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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2732-2736

Human-machine interactive reinforcement learning method for autonomous driving based on PPO algorithm

Shi Gaosong
Zhao Qinghai
Dong Xin
He Jiahao
Liu Jiayuan
College of Mechanical & Electrical Engineering, Qingdao University, Qingdao Shandong 266071, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0018

Abstract

To address the high computational demands and slow convergence faced by DRL in the field of autonomous driving, this paper integrated VAE with PPO algorithm. By adopting VAEs feature encoding technology, it effectively transformed semantic images obtained from the Carla simulator into state inputs, thus tackling the high computational load of DRL in handling complex autonomous driving tasks. To solve the issues of local optima and slow convergence in DRL training, it introduced a driving intervention mechanism and a driver-guided experience replay mechanism. These mechanisms applied driving interventions during the initial training phase and when the model encounters local optima, so as to enhance the models learning efficiency and generalization capability. Experimental validation, conducted in left-turn scenarios at intersections, shows that with the aid of the driving intervention mechanism, the models performance improves more rapidly in the initial training phase. Moreover, driving interventions when encountering local optima further enhance the models performance, with even more significant improvements observed in complex scenarios.

Foundation Support

国家自然科学基金资助项目(52175236)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0018
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2732-2736
Serial Number: 1001-3695(2024)09-023-2732-05

Publish History

[2024-05-13] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

时高松, 赵清海, 董鑫, 等. 基于PPO算法的自动驾驶人机交互式强化学习方法 [J]. 计算机应用研究, 2024, 41 (9): 2732-2736. (Shi Gaosong, Zhao Qinghai, Dong Xin, et al. Human-machine interactive reinforcement learning method for autonomous driving based on PPO algorithm [J]. Application Research of Computers, 2024, 41 (9): 2732-2736. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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