An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior
This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2022-07, Vol.23 (7), p.8477-8487 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 8487 |
|---|---|
| container_issue | 7 |
| container_start_page | 8477 |
| container_title | IEEE transactions on intelligent transportation systems |
| container_volume | 23 |
| creator | Cong, Sensen Wang, Wensa Liang, Jun Chen, Long Cai, Yingfeng |
| description | This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed. |
| doi_str_mv | 10.1109/TITS.2021.3082944 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9447293</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9447293</ieee_id><sourcerecordid>2688703570</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-c5c0057f0c04ac04b15cbd90cc88fbc4d63e398567de122491b35d7dbc0805083</originalsourceid><addsrcrecordid>eNo9UFtLwzAUDqLgnP4A8SXgc-dJ0rTpY523wUTE6WtI09Oto2s0bQf-e1M2fPg41-9cPkKuGcwYg-xutVh9zDhwNhOgeBbHJ2TCpFQRAEtOR5_HUQYSzslF121DNpaMTch73tJ86N3O9LWlX7ipbYM037u6NK1FOndt711DX12JDa2cpw-mXaN3Q0eXpsVovglx3a7pPW7Mvnb-kpxVpunw6min5PPpcTV_iZZvz4t5vowsz0QfWWkBZFqBhdgEFEzaoszAWqWqwsZlIlBkSiZpiYzzOGOFkGVaFhZUeEOJKbk9zP327mfArtdbN_g2rNQ8USoFIQOmhB26rHdd57HS377eGf-rGehROT0qp0fl9FG5wLk5cGpE_O8PlTRcLv4A2dtpEw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2688703570</pqid></control><display><type>article</type><title>An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior</title><source>IEEE Electronic Library (IEL)</source><creator>Cong, Sensen ; Wang, Wensa ; Liang, Jun ; Chen, Long ; Cai, Yingfeng</creator><creatorcontrib>Cong, Sensen ; Wang, Wensa ; Liang, Jun ; Chen, Long ; Cai, Yingfeng</creatorcontrib><description>This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2021.3082944</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Back propagation networks ; back propagation neural network ; Collision avoidance ; Collision dynamics ; dangerous lane-changing probability ; hidden Markov model ; Hidden Markov models ; Lane changing ; Linear quadratic regulator ; Markov chains ; Mathematical models ; mixed connected vehicle ; Neural networks ; Parameters ; Predictive models ; Robust control ; Stability criteria ; Steering ; Traffic safety ; Trajectory ; Wheels</subject><ispartof>IEEE transactions on intelligent transportation systems, 2022-07, Vol.23 (7), p.8477-8487</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-c5c0057f0c04ac04b15cbd90cc88fbc4d63e398567de122491b35d7dbc0805083</citedby><cites>FETCH-LOGICAL-c293t-c5c0057f0c04ac04b15cbd90cc88fbc4d63e398567de122491b35d7dbc0805083</cites><orcidid>0000-0002-0633-9887 ; 0000-0003-4475-0359 ; 0000-0002-0314-5016 ; 0000-0002-2079-3867</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9447293$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27915,27916,54749</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9447293$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Cong, Sensen</creatorcontrib><creatorcontrib>Wang, Wensa</creatorcontrib><creatorcontrib>Liang, Jun</creatorcontrib><creatorcontrib>Chen, Long</creatorcontrib><creatorcontrib>Cai, Yingfeng</creatorcontrib><title>An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.</description><subject>Back propagation networks</subject><subject>back propagation neural network</subject><subject>Collision avoidance</subject><subject>Collision dynamics</subject><subject>dangerous lane-changing probability</subject><subject>hidden Markov model</subject><subject>Hidden Markov models</subject><subject>Lane changing</subject><subject>Linear quadratic regulator</subject><subject>Markov chains</subject><subject>Mathematical models</subject><subject>mixed connected vehicle</subject><subject>Neural networks</subject><subject>Parameters</subject><subject>Predictive models</subject><subject>Robust control</subject><subject>Stability criteria</subject><subject>Steering</subject><subject>Traffic safety</subject><subject>Trajectory</subject><subject>Wheels</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UFtLwzAUDqLgnP4A8SXgc-dJ0rTpY523wUTE6WtI09Oto2s0bQf-e1M2fPg41-9cPkKuGcwYg-xutVh9zDhwNhOgeBbHJ2TCpFQRAEtOR5_HUQYSzslF121DNpaMTch73tJ86N3O9LWlX7ipbYM037u6NK1FOndt711DX12JDa2cpw-mXaN3Q0eXpsVovglx3a7pPW7Mvnb-kpxVpunw6min5PPpcTV_iZZvz4t5vowsz0QfWWkBZFqBhdgEFEzaoszAWqWqwsZlIlBkSiZpiYzzOGOFkGVaFhZUeEOJKbk9zP327mfArtdbN_g2rNQ8USoFIQOmhB26rHdd57HS377eGf-rGehROT0qp0fl9FG5wLk5cGpE_O8PlTRcLv4A2dtpEw</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Cong, Sensen</creator><creator>Wang, Wensa</creator><creator>Liang, Jun</creator><creator>Chen, Long</creator><creator>Cai, Yingfeng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-0633-9887</orcidid><orcidid>https://orcid.org/0000-0003-4475-0359</orcidid><orcidid>https://orcid.org/0000-0002-0314-5016</orcidid><orcidid>https://orcid.org/0000-0002-2079-3867</orcidid></search><sort><creationdate>20220701</creationdate><title>An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior</title><author>Cong, Sensen ; Wang, Wensa ; Liang, Jun ; Chen, Long ; Cai, Yingfeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-c5c0057f0c04ac04b15cbd90cc88fbc4d63e398567de122491b35d7dbc0805083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Back propagation networks</topic><topic>back propagation neural network</topic><topic>Collision avoidance</topic><topic>Collision dynamics</topic><topic>dangerous lane-changing probability</topic><topic>hidden Markov model</topic><topic>Hidden Markov models</topic><topic>Lane changing</topic><topic>Linear quadratic regulator</topic><topic>Markov chains</topic><topic>Mathematical models</topic><topic>mixed connected vehicle</topic><topic>Neural networks</topic><topic>Parameters</topic><topic>Predictive models</topic><topic>Robust control</topic><topic>Stability criteria</topic><topic>Steering</topic><topic>Traffic safety</topic><topic>Trajectory</topic><topic>Wheels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cong, Sensen</creatorcontrib><creatorcontrib>Wang, Wensa</creatorcontrib><creatorcontrib>Liang, Jun</creatorcontrib><creatorcontrib>Chen, Long</creatorcontrib><creatorcontrib>Cai, Yingfeng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cong, Sensen</au><au>Wang, Wensa</au><au>Liang, Jun</au><au>Chen, Long</au><au>Cai, Yingfeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2022-07-01</date><risdate>2022</risdate><volume>23</volume><issue>7</issue><spage>8477</spage><epage>8487</epage><pages>8477-8487</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2021.3082944</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0633-9887</orcidid><orcidid>https://orcid.org/0000-0003-4475-0359</orcidid><orcidid>https://orcid.org/0000-0002-0314-5016</orcidid><orcidid>https://orcid.org/0000-0002-2079-3867</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1524-9050 |
| ispartof | IEEE transactions on intelligent transportation systems, 2022-07, Vol.23 (7), p.8477-8487 |
| issn | 1524-9050 1558-0016 |
| language | eng |
| recordid | cdi_ieee_primary_9447293 |
| source | IEEE Electronic Library (IEL) |
| subjects | Back propagation networks back propagation neural network Collision avoidance Collision dynamics dangerous lane-changing probability hidden Markov model Hidden Markov models Lane changing Linear quadratic regulator Markov chains Mathematical models mixed connected vehicle Neural networks Parameters Predictive models Robust control Stability criteria Steering Traffic safety Trajectory Wheels |
| title | An Automatic Vehicle Avoidance Control Model for Dangerous Lane-Changing Behavior |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T05%3A31%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20Automatic%20Vehicle%20Avoidance%20Control%20Model%20for%20Dangerous%20Lane-Changing%20Behavior&rft.jtitle=IEEE%20transactions%20on%20intelligent%20transportation%20systems&rft.au=Cong,%20Sensen&rft.date=2022-07-01&rft.volume=23&rft.issue=7&rft.spage=8477&rft.epage=8487&rft.pages=8477-8487&rft.issn=1524-9050&rft.eissn=1558-0016&rft.coden=ITISFG&rft_id=info:doi/10.1109/TITS.2021.3082944&rft_dat=%3Cproquest_RIE%3E2688703570%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2688703570&rft_id=info:pmid/&rft_ieee_id=9447293&rfr_iscdi=true |