End-to-End Autonomous Driving Decision Based on Deep Reinforcement Learning - Details

Author：

Huang, Zhiqing (Huang, Zhiqing.) | Zhang, Ji (Zhang, Ji.) | Tian, Rui (Tian, Rui.) | Zhang, Yanxin (Zhang, Yanxin.)

Indexed by：

EI Scopus

Abstract：

End-to-end　autonomous　driving　decision-making　is　a　popular　research　field　in　autonomous　driving.　In　this　paper,　we　propose　an　end-to-end　decision-making　model　based　on　DDPG　deep　reinforcement　learning.　Firstly,　we　establish　an　end-to-end　decision-making　model　to　map　driving　state　(such　as　tangential　angle　of　vehicle,　velocity　of　vehicle,　distance　of　road)　to　driving　action　(steer,　accelerate,　brake)　continuously.　Next　we　train　and　valid　our　agent　in　different　scenarios　on　TORCS　platform.　The　results　show　that　DDPG　algorithms　can　achieve　end-to-end　autonomous　driving　decisions.　Finally,　we　visualize　the　agent　by　analyzing　which　state　contributes　to　decision.　©　2019　IEEE.

Keyword：

Reinforcement learning Robotics Deep learning Decision making Autonomous vehicles Agricultural robots

Author Community：

[ 1 ] [Huang, Zhiqing]Faculty of Information Technology, Beijing University Of Technology, Beijing, China
[ 2 ] [Zhang, Ji]Faculty of Information Technology, Beijing University Of Technology, Beijing, China
[ 3 ] [Tian, Rui]Faculty of Information Technology, Beijing University Of Technology, Beijing, China
[ 4 ] [Zhang, Yanxin]School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China

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End-to-End Autonomous Driving Decision Based on Deep Reinforcement Learning
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Source ：

Year： 2019

Page： 658-662

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 32

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 16

Affiliated Colleges：

信息科学技术学院本学院/部未明确归属的数据

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