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作者:

Zhang, Jin (Zhang, Jin.) | Ma, Nan (Ma, Nan.) | Wu, Zhixuan (Wu, Zhixuan.) | Wang, Cheng (Wang, Cheng.) | Yao, Yongqiang (Yao, Yongqiang.)

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EI Scopus

摘要:

Due to the complexity of the driving environment and the dynamics of the behavior of traffic participants, self-driving in dense traffic flow is very challenging. Traditional methods usually rely on predefined rules, which are difficult to adapt to various driving scenarios. Deep reinforcement learning (DRL) shows advantages over rule-based methods in complex self-driving environments, demonstrating the great potential of intelligent decision-making. However, one of the problems of DRL is the inefficiency of exploration; typically, it requires a lot of trial and error to learn the optimal policy, which leads to its slow learning rate and makes it difficult for the agent to learn well-performing decision-making policies in self-driving scenarios. Inspired by the outstanding performance of supervised learning in classification tasks, we propose a self-driving intelligent control method that combines human driving experience and adaptive sampling supervised actor-critic algorithm. Unlike traditional DRL, we modified the learning process of the policy network by combining supervised learning and DRL and adding human driving experience to the learning samples to better guide the self-driving vehicle to learn the optimal policy through human driving experience and real-time human guidance. In addition, in order to make the agent learn more efficiently, we introduced real-time human guidance in its learning process, and an adaptive balanced sampling method was designed for improving the sampling performance. We also designed the reward function in detail for different evaluation indexes such as traffic efficiency, which further guides the agent to learn the self-driving intelligent control policy in a better way. The experimental results show that the method is able to control vehicles in complex traffic environments for self-driving tasks and exhibits better performance than other DRL methods. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

关键词:

Reinforcement learning Supervised learning Automobile drivers Deep learning Learning algorithms Complex networks Decision making Intelligent control Learning systems Intelligent vehicle highway systems Vehicles

作者机构:

  • [ 1 ] [Zhang, Jin]Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing; 100101, China
  • [ 2 ] [Ma, Nan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Wu, Zhixuan]Beijing University of Posts and Telecommunications, Beijing; 100876, China
  • [ 4 ] [Wang, Cheng]Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing; 100101, China
  • [ 5 ] [Yao, Yongqiang]Beijing Shuncheng High Technology Corporation, Beijing; 102206, China

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来源 :

Mathematical Biosciences and Engineering

ISSN: 1547-1063

年份: 2024

期: 5

卷: 21

页码: 6077-6096

2 . 6 0 0

JCR@2022

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