The Model of Vehicle Position Estimation and Prediction Based on State-Space Approach
As one part of ITS (intelligent transportation system), IRS (intelligent road system) focus on improving the road safety and operation efficiency of highway system based on the idea of cooperation between road infrastructure and vehicles. Many IRS applications such as collision avoidance, automatic...
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| creator | Li Pingsheng Xie Xiaoli Li Bin Wang Meng |
| description | As one part of ITS (intelligent transportation system), IRS (intelligent road system) focus on improving the road safety and operation efficiency of highway system based on the idea of cooperation between road infrastructure and vehicles. Many IRS applications such as collision avoidance, automatic lane changing and others are principally based on the knowledge of the accurate geographical locations of interrelated vehicles nearby. Based on the state-space approach, this paper addresses the distributed position estimation problem. Specially, the state transfer matrix and measure matrix of the vehicle are established. And based on vehicle dynamics and Kalman filtering, the model of the position state estimation and prediction are formulated. Finally, we found that this approach can get more accurate results by the simulation under condition that the cooperative vehicle communication is available. |
| doi_str_mv | 10.1109/ICICTA.2009.664 |
| format | Conference Proceeding |
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Finally, we found that this approach can get more accurate results by the simulation under condition that the cooperative vehicle communication is available.</description><identifier>ISBN: 0769538045</identifier><identifier>ISBN: 9780769538044</identifier><identifier>DOI: 10.1109/ICICTA.2009.664</identifier><identifier>LCCN: 2009906273</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automated highways ; Collision avoidance ; estimation problem ; Intelligent Road System ; Intelligent transportation systems ; Intelligent vehicles ; Kalman filtering ; Predictive models ; Road safety ; Road transportation ; Road vehicles ; State estimation ; state-space approach ; Vehicle safety</subject><ispartof>2009 Second International Conference on Intelligent Computation Technology and Automation, 2009, Vol.3, p.822-825</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5288116$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5288116$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li Pingsheng</creatorcontrib><creatorcontrib>Xie Xiaoli</creatorcontrib><creatorcontrib>Li Bin</creatorcontrib><creatorcontrib>Wang Meng</creatorcontrib><title>The Model of Vehicle Position Estimation and Prediction Based on State-Space Approach</title><title>2009 Second International Conference on Intelligent Computation Technology and Automation</title><addtitle>ICICTA</addtitle><description>As one part of ITS (intelligent transportation system), IRS (intelligent road system) focus on improving the road safety and operation efficiency of highway system based on the idea of cooperation between road infrastructure and vehicles. 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Finally, we found that this approach can get more accurate results by the simulation under condition that the cooperative vehicle communication is available.</description><subject>Automated highways</subject><subject>Collision avoidance</subject><subject>estimation problem</subject><subject>Intelligent Road System</subject><subject>Intelligent transportation systems</subject><subject>Intelligent vehicles</subject><subject>Kalman filtering</subject><subject>Predictive models</subject><subject>Road safety</subject><subject>Road transportation</subject><subject>Road vehicles</subject><subject>State estimation</subject><subject>state-space approach</subject><subject>Vehicle safety</subject><isbn>0769538045</isbn><isbn>9780769538044</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjj1PwzAURS2hStDSmYHFfyDh-dseQ1QgUhGVmrJWJnlWjEITxVn495TCdM89w9Ul5I5Bzhi4h6qsyrrIOYDLtZZXZAlGOyUsSLUgy1_vQHMjrsk6pU8AYE4bpeGGHOoO6evQYk-HQN-xi02PdDekOMfhRDdpjl_-gv7U0t2EbWwu9dEnbOkZ9rOfMduPvkFajOM0-Ka7JYvg-4Tr_1yRw9OmLl-y7dtzVRbbLDKj5kwEwYKTynLXciNFoxo0Cj-Y9EZzrVtvvQmCSy-FABCGKycRRXAgrQ1SrMj9325ExOM4nb9O30fFrWVMix9J608u</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Li Pingsheng</creator><creator>Xie Xiaoli</creator><creator>Li Bin</creator><creator>Wang Meng</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200910</creationdate><title>The Model of Vehicle Position Estimation and Prediction Based on State-Space Approach</title><author>Li Pingsheng ; Xie Xiaoli ; Li Bin ; Wang Meng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-3f31f945829d2743c5ce75eb14a76266da8a7f324a43300372594ee3f90488f43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Automated highways</topic><topic>Collision avoidance</topic><topic>estimation problem</topic><topic>Intelligent Road System</topic><topic>Intelligent transportation systems</topic><topic>Intelligent vehicles</topic><topic>Kalman filtering</topic><topic>Predictive models</topic><topic>Road safety</topic><topic>Road transportation</topic><topic>Road vehicles</topic><topic>State estimation</topic><topic>state-space approach</topic><topic>Vehicle safety</topic><toplevel>online_resources</toplevel><creatorcontrib>Li Pingsheng</creatorcontrib><creatorcontrib>Xie Xiaoli</creatorcontrib><creatorcontrib>Li Bin</creatorcontrib><creatorcontrib>Wang Meng</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Pingsheng</au><au>Xie Xiaoli</au><au>Li Bin</au><au>Wang Meng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The Model of Vehicle Position Estimation and Prediction Based on State-Space Approach</atitle><btitle>2009 Second International Conference on Intelligent Computation Technology and Automation</btitle><stitle>ICICTA</stitle><date>2009-10</date><risdate>2009</risdate><volume>3</volume><spage>822</spage><epage>825</epage><pages>822-825</pages><isbn>0769538045</isbn><isbn>9780769538044</isbn><abstract>As one part of ITS (intelligent transportation system), IRS (intelligent road system) focus on improving the road safety and operation efficiency of highway system based on the idea of cooperation between road infrastructure and vehicles. Many IRS applications such as collision avoidance, automatic lane changing and others are principally based on the knowledge of the accurate geographical locations of interrelated vehicles nearby. Based on the state-space approach, this paper addresses the distributed position estimation problem. Specially, the state transfer matrix and measure matrix of the vehicle are established. And based on vehicle dynamics and Kalman filtering, the model of the position state estimation and prediction are formulated. Finally, we found that this approach can get more accurate results by the simulation under condition that the cooperative vehicle communication is available.</abstract><pub>IEEE</pub><doi>10.1109/ICICTA.2009.664</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2009 Second International Conference on Intelligent Computation Technology and Automation, 2009, Vol.3, p.822-825 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automated highways Collision avoidance estimation problem Intelligent Road System Intelligent transportation systems Intelligent vehicles Kalman filtering Predictive models Road safety Road transportation Road vehicles State estimation state-space approach Vehicle safety |
| title | The Model of Vehicle Position Estimation and Prediction Based on State-Space Approach |
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