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Full-range adaptive cruise control based on supervised adaptive dynamic programming
The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in...
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Published in: | Neurocomputing (Amsterdam) 2014-02, Vol.125, p.57-67 |
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container_title | Neurocomputing (Amsterdam) |
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creator | Zhao, Dongbin Hu, Zhaohui Xia, Zhongpu Alippi, Cesare Zhu, Yuanheng Wang, Ding |
description | The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem. |
doi_str_mv | 10.1016/j.neucom.2012.09.034 |
format | article |
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Systems ; Dynamic programming ; Dynamical systems ; Dynamics ; Exact sciences and technology ; Ground, air and sea transportation, marine construction ; Learning and adaptive systems ; Neural networks ; Road transportation and traffic ; Robotics ; Stop and go ; Supervised reinforcement learning ; Theoretical computing ; Training ; Vehicles</subject><ispartof>Neurocomputing (Amsterdam), 2014-02, Vol.125, p.57-67</ispartof><rights>2013 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-daa38cc90126e989d7715482e48bf0bab72aea04d5fec4f950fef4837ee25a793</citedby><cites>FETCH-LOGICAL-c415t-daa38cc90126e989d7715482e48bf0bab72aea04d5fec4f950fef4837ee25a793</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,776,780,785,786,23909,23910,25118,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28284342$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Dongbin</creatorcontrib><creatorcontrib>Hu, Zhaohui</creatorcontrib><creatorcontrib>Xia, Zhongpu</creatorcontrib><creatorcontrib>Alippi, Cesare</creatorcontrib><creatorcontrib>Zhu, Yuanheng</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><title>Full-range adaptive cruise control based on supervised adaptive dynamic programming</title><title>Neurocomputing (Amsterdam)</title><description>The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem.</description><subject>Adaptive algorithms</subject><subject>Adaptive control systems</subject><subject>Adaptive cruise control</subject><subject>Adaptive dynamic programming</subject><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Dynamic programming</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Exact sciences and technology</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Learning and adaptive systems</subject><subject>Neural networks</subject><subject>Road transportation and traffic</subject><subject>Robotics</subject><subject>Stop and go</subject><subject>Supervised reinforcement learning</subject><subject>Theoretical computing</subject><subject>Training</subject><subject>Vehicles</subject><issn>0925-2312</issn><issn>1872-8286</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-Aw-9CF5akzRtk4sgi6vCggf1HKbpdMnSL5N2Yf-9Wbrs0dMw8My8Mw8h94wmjLL8aZd0OJm-TThlPKEqoam4IAsmCx5LLvNLsqCKZzFPGb8mN97vKGUF42pBvtZT08QOui1GUMEw2j1Gxk3Wh9J3o-ubqASPVdR3kZ8GdHt77M5sdeigtSYaXL910La2296Sqxoaj3enuiQ_69fv1Xu8-Xz7WL1sYiNYNsYVQCqNUeHmHJVUVVGwTEiOQpY1LaEsOCBQUWU1GlGrjNZYC5kWiDyDQqVL8jjvDdm_E_pRt9YbbBrosJ-8ZnlYmHKZZQEVM2pc773DWg_OtuAOmlF9dKh3enaojw41VTo4DGMPpwTwBpo6eDLWn2d5kCtSwQP3PHMY3t1bdNobi53Byjo0o656-3_QH2OUivM</recordid><startdate>20140211</startdate><enddate>20140211</enddate><creator>Zhao, Dongbin</creator><creator>Hu, Zhaohui</creator><creator>Xia, Zhongpu</creator><creator>Alippi, Cesare</creator><creator>Zhu, Yuanheng</creator><creator>Wang, Ding</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140211</creationdate><title>Full-range adaptive cruise control based on supervised adaptive dynamic programming</title><author>Zhao, Dongbin ; Hu, Zhaohui ; Xia, Zhongpu ; Alippi, Cesare ; Zhu, Yuanheng ; Wang, Ding</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-daa38cc90126e989d7715482e48bf0bab72aea04d5fec4f950fef4837ee25a793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive control systems</topic><topic>Adaptive cruise control</topic><topic>Adaptive dynamic programming</topic><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Control theory. Systems</topic><topic>Dynamic programming</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>Exact sciences and technology</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Learning and adaptive systems</topic><topic>Neural networks</topic><topic>Road transportation and traffic</topic><topic>Robotics</topic><topic>Stop and go</topic><topic>Supervised reinforcement learning</topic><topic>Theoretical computing</topic><topic>Training</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Dongbin</creatorcontrib><creatorcontrib>Hu, Zhaohui</creatorcontrib><creatorcontrib>Xia, Zhongpu</creatorcontrib><creatorcontrib>Alippi, Cesare</creatorcontrib><creatorcontrib>Zhu, Yuanheng</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Neurocomputing (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Dongbin</au><au>Hu, Zhaohui</au><au>Xia, Zhongpu</au><au>Alippi, Cesare</au><au>Zhu, Yuanheng</au><au>Wang, Ding</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Full-range adaptive cruise control based on supervised adaptive dynamic programming</atitle><jtitle>Neurocomputing (Amsterdam)</jtitle><date>2014-02-11</date><risdate>2014</risdate><volume>125</volume><spage>57</spage><epage>67</epage><pages>57-67</pages><issn>0925-2312</issn><eissn>1872-8286</eissn><abstract>The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.neucom.2012.09.034</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adaptive algorithms Adaptive control systems Adaptive cruise control Adaptive dynamic programming Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Computer science control theory systems Control theory. Systems Dynamic programming Dynamical systems Dynamics Exact sciences and technology Ground, air and sea transportation, marine construction Learning and adaptive systems Neural networks Road transportation and traffic Robotics Stop and go Supervised reinforcement learning Theoretical computing Training Vehicles |
title | Full-range adaptive cruise control based on supervised adaptive dynamic programming |
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