A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning

Following man-made rules in the traditional control method of autonomous driving causes limitations for intelligent vehicles under various traffic conditions that need to be overcome by incorporating machine learning-based method. The latter is inherently suitable for simple tasks of autonomous driv...

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Autores principales:	Yingfeng Cai, Shaoqing Yang, Hai Wang, Chenglong Teng, Long Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Autonomous driving reinforcement learning multi-task exploration method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b36ef47ce6f74f81b0b9f312e917770c
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id	oai:doaj.org-article:b36ef47ce6f74f81b0b9f312e917770c
record_format	dspace
spelling	oai:doaj.org-article:b36ef47ce6f74f81b0b9f312e917770c2021-11-25T00:00:47ZA Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning2169-353610.1109/ACCESS.2021.3126796https://doaj.org/article/b36ef47ce6f74f81b0b9f312e917770c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9609985/https://doaj.org/toc/2169-3536Following man-made rules in the traditional control method of autonomous driving causes limitations for intelligent vehicles under various traffic conditions that need to be overcome by incorporating machine learning-based method. The latter is inherently suitable for simple tasks of autonomous driving according to its limited characteristic under complex multi-lane traffic conditions. In this paper, a decision control method is proposed based on multi-task reinforcement learning to address the shortcomings of autonomous driving control under complex traffic conditions. Herein, the autonomous driving task is divided into several subtasks utilizing the proposed method to reduce the training time and improve traffic efficiency under complex multi-lane traffic condition. To ensure the efficiency and robustness of agent convergence to the optimal action space, an adaptive noise exploration method is designed for the subtasks with convex characteristics. Five-lane driving tasks scenarios embedded in Carla simulator have been conducted to verify the proposed method. The results of the simulation draw the conclusion that the proposed method increases the driving efficiency of intelligent vehicles under complex traffic conditions.Yingfeng CaiShaoqing YangHai WangChenglong TengLong ChenIEEEarticleAutonomous drivingreinforcement learningmulti-taskexploration methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154553-154562 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Autonomous driving reinforcement learning multi-task exploration method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Autonomous driving reinforcement learning multi-task exploration method Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yingfeng Cai Shaoqing Yang Hai Wang Chenglong Teng Long Chen A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
description	Following man-made rules in the traditional control method of autonomous driving causes limitations for intelligent vehicles under various traffic conditions that need to be overcome by incorporating machine learning-based method. The latter is inherently suitable for simple tasks of autonomous driving according to its limited characteristic under complex multi-lane traffic conditions. In this paper, a decision control method is proposed based on multi-task reinforcement learning to address the shortcomings of autonomous driving control under complex traffic conditions. Herein, the autonomous driving task is divided into several subtasks utilizing the proposed method to reduce the training time and improve traffic efficiency under complex multi-lane traffic condition. To ensure the efficiency and robustness of agent convergence to the optimal action space, an adaptive noise exploration method is designed for the subtasks with convex characteristics. Five-lane driving tasks scenarios embedded in Carla simulator have been conducted to verify the proposed method. The results of the simulation draw the conclusion that the proposed method increases the driving efficiency of intelligent vehicles under complex traffic conditions.
format	article
author	Yingfeng Cai Shaoqing Yang Hai Wang Chenglong Teng Long Chen
author_facet	Yingfeng Cai Shaoqing Yang Hai Wang Chenglong Teng Long Chen
author_sort	Yingfeng Cai
title	A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
title_short	A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
title_full	A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
title_fullStr	A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
title_full_unstemmed	A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning
title_sort	decision control method for autonomous driving based on multi-task reinforcement learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b36ef47ce6f74f81b0b9f312e917770c
work_keys_str_mv	AT yingfengcai adecisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT shaoqingyang adecisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT haiwang adecisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT chenglongteng adecisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT longchen adecisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT yingfengcai decisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT shaoqingyang decisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT haiwang decisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT chenglongteng decisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning AT longchen decisioncontrolmethodforautonomousdrivingbasedonmultitaskreinforcementlearning
_version_	1718414713809797120

A Decision Control Method for Autonomous Driving Based on Multi-Task Reinforcement Learning

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