典型文献
Vehicle-following system based on deep reinforcement learning in marine scene
文献摘要：
In order to solve the problems that the feature data type are not rich enough in the data collection process about the vehicle-following task in marine scene which results in a long model convergence time and high training difficulty,a two-stage vehicle-following system was proposed.Firstly,semantic segmentation model predicts the number of pixels of the followed target,then the number of pixels of the followed target is mapped to the position feature.Secondly,deep reinforcement learning algorithm enables the control equipment to make decision action,to ensure that two moving objects remain within the safe distance.The experimental results show that the two-stage vehicle-following system has a 40％faster convergence rate than the model without position feature,and the following stability is significantly improved by adding the position feature.
文献关键词：
中图分类号：
作者姓名：
Zhang Xin;Lou Haoran;Jiang Li;Xiao Qianhao;Cai Zhuwen
作者机构：
School of Electronic Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710061,China
文献出处：
引用格式：
[1]Zhang Xin;Lou Haoran;Jiang Li;Xiao Qianhao;Cai Zhuwen-.Vehicle-following system based on deep reinforcement learning in marine scene)[J].中国邮电高校学报（英文版）,2022(05):10-20
A类：
B类：
Vehicle,following,system,deep,reinforcement,learning,marine,scene,In,order,solve,problems,that,feature,data,type,are,not,rich,enough,collection,process,about,vehicle,task,which,results,long,model,convergence,high,training,difficulty,two,stage,was,proposed,Firstly,semantic,segmentation,predicts,number,pixels,followed,target,then,mapped,position,Secondly,algorithm,enables,control,equipment,make,decision,action,ensure,moving,objects,remain,within,safe,distance,experimental,show,has,faster,rate,than,without,stability,significantly,improved,by,adding
AB值：
0.602673
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