A collision model for safety evaluation of autonomous intelligent cruise control
Identifieur interne : 002771 ( Istex/Corpus ); précédent : 002770; suivant : 002772A collision model for safety evaluation of autonomous intelligent cruise control
Auteurs : Ali Touran ; Mark A. Brackstone ; Mike McdonaldSource :
- Accident Analysis and Prevention [ 0001-4575 ] ; 1999.
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Abstract
This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.
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DOI: 10.1016/S0001-4575(99)00013-5
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A collision model for safety evaluation of autonomous intelligent cruise control</title><author><name sortKey="Touran, Ali" sort="Touran, Ali" uniqKey="Touran A" first="Ali" last="Touran">Ali Touran</name><affiliation><mods:affiliation>E-mail: atouran@lynx.dac.neu.edu</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</mods:affiliation></affiliation></author><author><name sortKey="Brackstone, Mark A" sort="Brackstone, Mark A" uniqKey="Brackstone M" first="Mark A" last="Brackstone">Mark A. Brackstone</name><affiliation><mods:affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</mods:affiliation></affiliation></author><author><name sortKey="Mcdonald, Mike" sort="Mcdonald, Mike" uniqKey="Mcdonald M" first="Mike" last="Mcdonald">Mike Mcdonald</name><affiliation><mods:affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:EB10CA3B8052FA006895477089499599BC19760B</idno><date when="1999" year="1999">1999</date><idno type="doi">10.1016/S0001-4575(99)00013-5</idno><idno type="url">https://api.istex.fr/document/EB10CA3B8052FA006895477089499599BC19760B/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002771</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">002771</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A collision model for safety evaluation of autonomous intelligent cruise control</title><author><name sortKey="Touran, Ali" sort="Touran, Ali" uniqKey="Touran A" first="Ali" last="Touran">Ali Touran</name><affiliation><mods:affiliation>E-mail: atouran@lynx.dac.neu.edu</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</mods:affiliation></affiliation></author><author><name sortKey="Brackstone, Mark A" sort="Brackstone, Mark A" uniqKey="Brackstone M" first="Mark A" last="Brackstone">Mark A. Brackstone</name><affiliation><mods:affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</mods:affiliation></affiliation></author><author><name sortKey="Mcdonald, Mike" sort="Mcdonald, Mike" uniqKey="Mcdonald M" first="Mike" last="Mcdonald">Mike Mcdonald</name><affiliation><mods:affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Accident Analysis and Prevention</title><title level="j" type="abbrev">AAP</title><idno type="ISSN">0001-4575</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1999">1999</date><biblScope unit="volume">31</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="567">567</biblScope><biblScope unit="page" to="578">578</biblScope></imprint><idno type="ISSN">0001-4575</idno></series><idno type="istex">EB10CA3B8052FA006895477089499599BC19760B</idno><idno type="DOI">10.1016/S0001-4575(99)00013-5</idno><idno type="PII">S0001-4575(99)00013-5</idno><idno type="ArticleID">500</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0001-4575</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Autonomous intelligent cruise control (AICC)</term><term>Collision</term><term>Human behaviour</term><term>Monte Carlo simulation</term><term>Risk assessment</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Ali Touran</name><affiliations><json:string>E-mail: atouran@lynx.dac.neu.edu</json:string><json:string>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</json:string></affiliations></json:item><json:item><name>Mark A Brackstone</name><affiliations><json:string>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</json:string></affiliations></json:item><json:item><name>Mike McDonald</name><affiliations><json:string>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Autonomous intelligent cruise control (AICC)</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Collision</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Monte Carlo simulation</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Risk assessment</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>Human behaviour</value></json:item></subject><articleId><json:string>500</json:string></articleId><language><json:string>eng</json:string></language><abstract>This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.</abstract><qualityIndicators><score>7.292</score><pdfVersion>1.2</pdfVersion><pdfPageSize>551 x 768 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>5</keywordCount><abstractCharCount>1257</abstractCharCount><pdfWordCount>6927</pdfWordCount><pdfCharCount>40243</pdfCharCount><pdfPageCount>12</pdfPageCount><abstractWordCount>191</abstractWordCount></qualityIndicators><title>A collision model for safety evaluation of autonomous intelligent cruise control</title><pii><json:string>S0001-4575(99)00013-5</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>31</volume><pii><json:string>S0001-4575(00)X0028-0</json:string></pii><pages><last>578</last><first>567</first></pages><issn><json:string>0001-4575</json:string></issn><issue>5</issue><genre><json:string>Journal</json:string></genre><language><json:string>unknown</json:string></language><title>Accident Analysis and Prevention</title><publicationDate>1999</publicationDate></host><publicationDate>1999</publicationDate><copyrightDate>1999</copyrightDate><doi><json:string>10.1016/S0001-4575(99)00013-5</json:string></doi><id>EB10CA3B8052FA006895477089499599BC19760B</id><score>1</score><fulltext><json:item><original>true</original><mimetype>application/pdf</mimetype><extension>pdf</extension><uri>https://api.istex.fr/document/EB10CA3B8052FA006895477089499599BC19760B/fulltext/pdf</uri></json:item><json:item><original>true</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/EB10CA3B8052FA006895477089499599BC19760B/fulltext/txt</uri></json:item><json:item><original>false</original><mimetype>application/zip</mimetype><extension>zip</extension><uri>https://api.istex.fr/document/EB10CA3B8052FA006895477089499599BC19760B/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/EB10CA3B8052FA006895477089499599BC19760B/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">A collision model for safety evaluation of autonomous intelligent cruise control</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>ELSEVIER</p></availability><date>1999</date></publicationStmt><notesStmt><note type="content">Fig. 1: Car following model for the car equipped with AICC.</note><note type="content">Fig. 2: Car following model with no AICC.</note><note type="content">Fig. 3: AICC model output for each pair of following cars.</note><note type="content">Table 1: Input data for the model parameters</note><note type="content">Table 2: Impact of PRT on the required headway</note><note type="content">Table 3: Multi-car collision statistics</note><note type="content">Table 4: Probabilities of crash avoidance with various emergency braking rates</note><note type="content">Table 5: Contribution of input parameters to the variance of the model’s outcome</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">A collision model for safety evaluation of autonomous intelligent cruise control</title><author><persName><forename type="first">Ali</forename><surname>Touran</surname></persName><email>atouran@lynx.dac.neu.edu</email><note type="correspondence"><p>Corresponding author. Tel.: +1-617-3732444; fax: +1-617-3734419</p></note><affiliation>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</affiliation></author><author><persName><forename type="first">Mark A</forename><surname>Brackstone</surname></persName><affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</affiliation></author><author><persName><forename type="first">Mike</forename><surname>McDonald</surname></persName><affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</affiliation></author></analytic><monogr><title level="j">Accident Analysis and Prevention</title><title level="j" type="abbrev">AAP</title><idno type="pISSN">0001-4575</idno><idno type="PII">S0001-4575(00)X0028-0</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1999"></date><biblScope unit="volume">31</biblScope><biblScope unit="issue">5</biblScope><biblScope unit="page" from="567">567</biblScope><biblScope unit="page" to="578">578</biblScope></imprint></monogr><idno type="istex">EB10CA3B8052FA006895477089499599BC19760B</idno><idno type="DOI">10.1016/S0001-4575(99)00013-5</idno><idno type="PII">S0001-4575(99)00013-5</idno><idno type="ArticleID">500</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1999</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Autonomous intelligent cruise control (AICC)</term></item><item><term>Collision</term></item><item><term>Monte Carlo simulation</term></item><item><term>Risk assessment</term></item><item><term>Human behaviour</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1999-01-19">Registration</change><change when="1999-01-14">Modified</change><change when="1999">Published</change></revisionDesc></teiHeader></istex:fulltextTEI></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla" xml:lang="en"><item-info><jid>AAP</jid><aid>500</aid><ce:pii>S0001-4575(99)00013-5</ce:pii><ce:doi>10.1016/S0001-4575(99)00013-5</ce:doi><ce:copyright type="full-transfer" year="1999">Elsevier Science Ltd</ce:copyright></item-info><head><ce:title>A collision model for safety evaluation of autonomous intelligent cruise control</ce:title><ce:author-group><ce:author><ce:given-name>Ali</ce:given-name><ce:surname>Touran</ce:surname><ce:cross-ref refid="AFF1"><ce:sup loc="post">a</ce:sup></ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref><ce:e-address type="email">atouran@lynx.dac.neu.edu</ce:e-address></ce:author><ce:author><ce:given-name>Mark A</ce:given-name><ce:surname>Brackstone</ce:surname><ce:cross-ref refid="AFF2"><ce:sup loc="post">b</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>Mike</ce:given-name><ce:surname>McDonald</ce:surname><ce:cross-ref refid="AFF2"><ce:sup loc="post">b</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Corresponding author. Tel.: +1-617-3732444; fax: +1-617-3734419</ce:text></ce:correspondence></ce:author-group><ce:date-received day="14" month="6" year="1998"></ce:date-received><ce:date-revised day="14" month="1" year="1999"></ce:date-revised><ce:date-accepted day="19" month="1" year="1999"></ce:date-accepted><ce:abstract class="author"><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para view="all" id="simple-para.0050">This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Autonomous intelligent cruise control (AICC)</ce:text></ce:keyword><ce:keyword><ce:text>Collision</ce:text></ce:keyword><ce:keyword><ce:text>Monte Carlo simulation</ce:text></ce:keyword><ce:keyword><ce:text>Risk assessment</ce:text></ce:keyword><ce:keyword><ce:text>Human behaviour</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A collision model for safety evaluation of autonomous intelligent cruise control</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>A collision model for safety evaluation of autonomous intelligent cruise control</title></titleInfo><name type="personal"><namePart type="given">Ali</namePart><namePart type="family">Touran</namePart><affiliation>E-mail: atouran@lynx.dac.neu.edu</affiliation><affiliation>Department of Civil and Environmental Engineering, Northeastern University, 420 Snell Engineering Centre, Boston, MA 02115, USA</affiliation><description>Corresponding author. Tel.: +1-617-3732444; fax: +1-617-3734419</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Mark A</namePart><namePart type="family">Brackstone</namePart><affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Mike</namePart><namePart type="family">McDonald</namePart><affiliation>Transportation Research Group, University of Southampton, Southampton, SO17 1BJ, UK</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1999</dateIssued><dateValid encoding="w3cdtf">1999-01-19</dateValid><dateModified encoding="w3cdtf">1999-01-14</dateModified><copyrightDate encoding="w3cdtf">1999</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">This paper describes a general framework for safety evaluation of autonomous intelligent cruise control in rear-end collisions. Using data and specifications from prototype devices, two collision models are developed. One model considers a train of four cars, one of which is equipped with autonomous intelligent cruise control. This model considers the car in front and two cars following the equipped car. In the second model, none of the cars is equipped with the device. Each model can predict the possibility of rear-end collision between cars under various conditions by calculating the remaining distance between cars after the front car brakes. Comparing the two collision models allows one to evaluate the effectiveness of autonomous intelligent cruise control in preventing collisions. The models are then subjected to Monte Carlo simulation to calculate the probability of collision. Based on crash probabilities, an expected value is calculated for the number of cars involved in any collision. It is found that given the model assumptions, while equipping a car with autonomous intelligent cruise control can significantly reduce the probability of the collision with the car ahead, it may adversely affect the situation for the following cars.</abstract><note type="content">Fig. 1: Car following model for the car equipped with AICC.</note><note type="content">Fig. 2: Car following model with no AICC.</note><note type="content">Fig. 3: AICC model output for each pair of following cars.</note><note type="content">Table 1: Input data for the model parameters</note><note type="content">Table 2: Impact of PRT on the required headway</note><note type="content">Table 3: Multi-car collision statistics</note><note type="content">Table 4: Probabilities of crash avoidance with various emergency braking rates</note><note type="content">Table 5: Contribution of input parameters to the variance of the model’s outcome</note><subject lang="en"><genre>Keywords</genre><topic>Autonomous intelligent cruise control (AICC)</topic><topic>Collision</topic><topic>Monte Carlo simulation</topic><topic>Risk assessment</topic><topic>Human behaviour</topic></subject><relatedItem type="host"><titleInfo><title>Accident Analysis and Prevention</title></titleInfo><titleInfo type="abbreviated"><title>AAP</title></titleInfo><genre type="Journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199909</dateIssued></originInfo><identifier type="ISSN">0001-4575</identifier><identifier type="PII">S0001-4575(00)X0028-0</identifier><part><date>199909</date><detail type="volume"><number>31</number><caption>vol.</caption></detail><detail type="issue"><number>5</number><caption>no.</caption></detail><extent unit="issue pages"><start>421</start><end>600</end></extent><extent unit="pages"><start>567</start><end>578</end></extent></part></relatedItem><identifier type="istex">EB10CA3B8052FA006895477089499599BC19760B</identifier><identifier type="DOI">10.1016/S0001-4575(99)00013-5</identifier><identifier type="PII">S0001-4575(99)00013-5</identifier><identifier type="ArticleID">500</identifier><accessCondition type="use and reproduction" contentType="">© 1999Elsevier Science Ltd</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Ltd, ©1999</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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