Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to incr...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-14
Main Authors: Yang, Jiafu, Bu, Shengqiang, Li, Qiongqiong, Shi, Yangyang, Zhu, Linfeng
Format: Article
Language:English
Subjects:
Algorithms
B spline functions
Computer simulation
Convergence
Deviation
Driving conditions
Efficiency
Feasibility
Intelligent vehicles
Iterative methods
Mathematical problems
Optimization
Path planning
Randomness
Steering
Vehicles
Velocity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3
cites cdi_FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3
container_end_page 14
container_issue 2020
container_start_page 1
container_title Mathematical problems in engineering
container_volume 2020
creator Yang, Jiafu
Bu, Shengqiang
Li, Qiongqiong
Shi, Yangyang
Zhu, Linfeng
description Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.
doi_str_mv 10.1155/2020/5910503
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2386138127</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2386138127</sourcerecordid><originalsourceid>FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3</originalsourceid><addsrcrecordid>eNqF0E1Lw0AQBuBFFKzVm2cJeNTYnd1sPo5aqhYKllLFW9jsTpqUdBN3U7T_3oQUPHqagXmYYV5CroE-AAgxYZTRiUiACspPyAhEyH0BQXTa9ZQFPjD-eU4unNtSykBAPCLrFTqUVhVebby5abGqyg2a1vvAolQVekvZFt6yksaUZuM9SYe6pyvZlLo6-LOfpqptP1pJo-udt7aIl-Qsl5XDq2Mdk_fn2Xr66i_eXubTx4WveEhbn2UJxgiKo6SQUZWFQmoexEpQDVmuo5jpMM5EJBEykQRJnjOddL9RLqjKkY_J7bC3sfXXHl2bbuu9Nd3JlPE4BB4Dizp1Pyhla-cs5mljy520hxRo2ueW9rmlx9w6fjfwojRafpf_6ZtBY2cwl38akpAnnP8CB-h2Jg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2386138127</pqid></control><display><type>article</type><title>Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree</title><source>Wiley Online Library Open Access</source><source>Publicly Available Content Database</source><creator>Yang, Jiafu ; Bu, Shengqiang ; Li, Qiongqiong ; Shi, Yangyang ; Zhu, Linfeng</creator><contributor>Yang, Mijia</contributor><creatorcontrib>Yang, Jiafu ; Bu, Shengqiang ; Li, Qiongqiong ; Shi, Yangyang ; Zhu, Linfeng ; Yang, Mijia</creatorcontrib><description>Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/5910503</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; B spline functions ; Computer simulation ; Convergence ; Deviation ; Driving conditions ; Efficiency ; Feasibility ; Intelligent vehicles ; Iterative methods ; Mathematical problems ; Optimization ; Path planning ; Randomness ; Steering ; Vehicles ; Velocity</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-14</ispartof><rights>Copyright © 2020 Yangyang Shi et al.</rights><rights>Copyright © 2020 Yangyang Shi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3</citedby><cites>FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3</cites><orcidid>0000-0002-8264-9801 ; 0000-0002-5181-0973 ; 0000-0001-7873-0247 ; 0000-0002-3462-9187 ; 0000-0001-5631-3020</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2386138127/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2386138127?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml></links><search><contributor>Yang, Mijia</contributor><creatorcontrib>Yang, Jiafu</creatorcontrib><creatorcontrib>Bu, Shengqiang</creatorcontrib><creatorcontrib>Li, Qiongqiong</creatorcontrib><creatorcontrib>Shi, Yangyang</creatorcontrib><creatorcontrib>Zhu, Linfeng</creatorcontrib><title>Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree</title><title>Mathematical problems in engineering</title><description>Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.</description><subject>Algorithms</subject><subject>B spline functions</subject><subject>Computer simulation</subject><subject>Convergence</subject><subject>Deviation</subject><subject>Driving conditions</subject><subject>Efficiency</subject><subject>Feasibility</subject><subject>Intelligent vehicles</subject><subject>Iterative methods</subject><subject>Mathematical problems</subject><subject>Optimization</subject><subject>Path planning</subject><subject>Randomness</subject><subject>Steering</subject><subject>Vehicles</subject><subject>Velocity</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqF0E1Lw0AQBuBFFKzVm2cJeNTYnd1sPo5aqhYKllLFW9jsTpqUdBN3U7T_3oQUPHqagXmYYV5CroE-AAgxYZTRiUiACspPyAhEyH0BQXTa9ZQFPjD-eU4unNtSykBAPCLrFTqUVhVebby5abGqyg2a1vvAolQVekvZFt6yksaUZuM9SYe6pyvZlLo6-LOfpqptP1pJo-udt7aIl-Qsl5XDq2Mdk_fn2Xr66i_eXubTx4WveEhbn2UJxgiKo6SQUZWFQmoexEpQDVmuo5jpMM5EJBEykQRJnjOddL9RLqjKkY_J7bC3sfXXHl2bbuu9Nd3JlPE4BB4Dizp1Pyhla-cs5mljy520hxRo2ueW9rmlx9w6fjfwojRafpf_6ZtBY2cwl38akpAnnP8CB-h2Jg</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Yang, Jiafu</creator><creator>Bu, Shengqiang</creator><creator>Li, Qiongqiong</creator><creator>Shi, Yangyang</creator><creator>Zhu, Linfeng</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-8264-9801</orcidid><orcidid>https://orcid.org/0000-0002-5181-0973</orcidid><orcidid>https://orcid.org/0000-0001-7873-0247</orcidid><orcidid>https://orcid.org/0000-0002-3462-9187</orcidid><orcidid>https://orcid.org/0000-0001-5631-3020</orcidid></search><sort><creationdate>2020</creationdate><title>Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree</title><author>Yang, Jiafu ; Bu, Shengqiang ; Li, Qiongqiong ; Shi, Yangyang ; Zhu, Linfeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>B spline functions</topic><topic>Computer simulation</topic><topic>Convergence</topic><topic>Deviation</topic><topic>Driving conditions</topic><topic>Efficiency</topic><topic>Feasibility</topic><topic>Intelligent vehicles</topic><topic>Iterative methods</topic><topic>Mathematical problems</topic><topic>Optimization</topic><topic>Path planning</topic><topic>Randomness</topic><topic>Steering</topic><topic>Vehicles</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Jiafu</creatorcontrib><creatorcontrib>Bu, Shengqiang</creatorcontrib><creatorcontrib>Li, Qiongqiong</creatorcontrib><creatorcontrib>Shi, Yangyang</creatorcontrib><creatorcontrib>Zhu, Linfeng</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Materials Science &amp; Engineering</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied &amp; Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Jiafu</au><au>Bu, Shengqiang</au><au>Li, Qiongqiong</au><au>Shi, Yangyang</au><au>Zhu, Linfeng</au><addau>Yang, Mijia</addau><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2020/5910503</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-8264-9801</orcidid><orcidid>https://orcid.org/0000-0002-5181-0973</orcidid><orcidid>https://orcid.org/0000-0001-7873-0247</orcidid><orcidid>https://orcid.org/0000-0002-3462-9187</orcidid><orcidid>https://orcid.org/0000-0001-5631-3020</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1024-123X
ispartof Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-14
issn 1024-123X
1563-5147
language eng
recordid cdi_proquest_journals_2386138127
source Wiley Online Library Open Access; Publicly Available Content Database
subjects Algorithms
B spline functions
Computer simulation
Convergence
Deviation
Driving conditions
Efficiency
Feasibility
Intelligent vehicles
Iterative methods
Mathematical problems
Optimization
Path planning
Randomness
Steering
Vehicles
Velocity
title Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-03-07T02%3A51%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Research%20on%20Intelligent%20Vehicle%20Path%20Planning%20Based%20on%20Rapidly-Exploring%20Random%20Tree&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Yang,%20Jiafu&rft.date=2020&rft.volume=2020&rft.issue=2020&rft.spage=1&rft.epage=14&rft.pages=1-14&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2020/5910503&rft_dat=%3Cproquest_cross%3E2386138127%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2386138127&rft_id=info:pmid/&rfr_iscdi=true