End-to-End Autonomous Driving Decision Based on Deep Reinforcement Learning - 文章详情页

作者：

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

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EI Scopus

摘要：

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.

关键词：

Agricultural robots Autonomous vehicles Decision making Deep learning Reinforcement learning Robotics

作者机构：

[ 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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