Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evalua...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2021-09, Vol.22 (9), p.5635-5647 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5647 |
|---|---|
| container_issue | 9 |
| container_start_page | 5635 |
| container_title | IEEE transactions on intelligent transportation systems |
| container_volume | 22 |
| creator | Feng, Shuo Feng, Yiheng Sun, Haowei Bao, Shan Zhang, Yi Liu, Henry X. |
| description | Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method. |
| doi_str_mv | 10.1109/TITS.2020.2988309 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9086089</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9086089</ieee_id><sourcerecordid>2568070289</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-7e3a902160e7b44a16480afe43cf829878d2094e246bac7f59c29e4e275dba5a3</originalsourceid><addsrcrecordid>eNo9UNFKwzAUDaLgnH6A-BLw1c6bNGkT30bRWRgorPoa0vZWO7Z2Ju2Df2_Khk_3nss553IOIbcMFoyBfizyYrPgwGHBtVIx6DMyY1KqCIAl59PORaRBwiW58n4brkIyNiOmQD-03RfdVNhZ1_Z03ZbOul-6wg6dHdq-o03vaNZ3HVYD1tR2NV2OQ7-3E_rE77baoX-g79YNNM-faGY90s0w1i36a3LR2J3Hm9Ock4-X5yJ7jdZvqzxbrqOK63iIUoytBs4SwLQUwrJEKLANirhqVEiUqpqDFshFUtoqbaQOOgw4lXVppY3n5P7oe3D9zxgymW0_ui68NFwmClLgSgcWO7Iq13vvsDEH1-5DWsPATD2aqUcz9WhOPQbN3VHTIuI_X4NKIDj-AYSIbeY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2568070289</pqid></control><display><type>article</type><title>Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies</title><source>IEEE Electronic Library (IEL)</source><creator>Feng, Shuo ; Feng, Yiheng ; Sun, Haowei ; Bao, Shan ; Zhang, Yi ; Liu, Henry X.</creator><creatorcontrib>Feng, Shuo ; Feng, Yiheng ; Sun, Haowei ; Bao, Shan ; Zhang, Yi ; Liu, Henry X.</creatorcontrib><description>Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2020.2988309</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Automation ; Car following ; Connected and automated vehicles ; Estimation ; Frequency measurement ; functionality ; Indexes ; Libraries ; Linear programming ; reinforcement learning ; Road tests ; Safety ; Testing ; testing scenario library</subject><ispartof>IEEE transactions on intelligent transportation systems, 2021-09, Vol.22 (9), p.5635-5647</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-7e3a902160e7b44a16480afe43cf829878d2094e246bac7f59c29e4e275dba5a3</citedby><cites>FETCH-LOGICAL-c293t-7e3a902160e7b44a16480afe43cf829878d2094e246bac7f59c29e4e275dba5a3</cites><orcidid>0000-0001-5656-3222 ; 0000-0002-2117-4427 ; 0000-0001-5526-866X ; 0000-0002-3685-9920</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9086089$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27922,27923,54756</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9086089$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Feng, Shuo</creatorcontrib><creatorcontrib>Feng, Yiheng</creatorcontrib><creatorcontrib>Sun, Haowei</creatorcontrib><creatorcontrib>Bao, Shan</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Liu, Henry X.</creatorcontrib><title>Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.</description><subject>Automation</subject><subject>Car following</subject><subject>Connected and automated vehicles</subject><subject>Estimation</subject><subject>Frequency measurement</subject><subject>functionality</subject><subject>Indexes</subject><subject>Libraries</subject><subject>Linear programming</subject><subject>reinforcement learning</subject><subject>Road tests</subject><subject>Safety</subject><subject>Testing</subject><subject>testing scenario library</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9UNFKwzAUDaLgnH6A-BLw1c6bNGkT30bRWRgorPoa0vZWO7Z2Ju2Df2_Khk_3nss553IOIbcMFoyBfizyYrPgwGHBtVIx6DMyY1KqCIAl59PORaRBwiW58n4brkIyNiOmQD-03RfdVNhZ1_Z03ZbOul-6wg6dHdq-o03vaNZ3HVYD1tR2NV2OQ7-3E_rE77baoX-g79YNNM-faGY90s0w1i36a3LR2J3Hm9Ock4-X5yJ7jdZvqzxbrqOK63iIUoytBs4SwLQUwrJEKLANirhqVEiUqpqDFshFUtoqbaQOOgw4lXVppY3n5P7oe3D9zxgymW0_ui68NFwmClLgSgcWO7Iq13vvsDEH1-5DWsPATD2aqUcz9WhOPQbN3VHTIuI_X4NKIDj-AYSIbeY</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Feng, Shuo</creator><creator>Feng, Yiheng</creator><creator>Sun, Haowei</creator><creator>Bao, Shan</creator><creator>Zhang, Yi</creator><creator>Liu, Henry X.</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/0000-0001-5656-3222</orcidid><orcidid>https://orcid.org/0000-0002-2117-4427</orcidid><orcidid>https://orcid.org/0000-0001-5526-866X</orcidid><orcidid>https://orcid.org/0000-0002-3685-9920</orcidid></search><sort><creationdate>20210901</creationdate><title>Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies</title><author>Feng, Shuo ; Feng, Yiheng ; Sun, Haowei ; Bao, Shan ; Zhang, Yi ; Liu, Henry X.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-7e3a902160e7b44a16480afe43cf829878d2094e246bac7f59c29e4e275dba5a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Automation</topic><topic>Car following</topic><topic>Connected and automated vehicles</topic><topic>Estimation</topic><topic>Frequency measurement</topic><topic>functionality</topic><topic>Indexes</topic><topic>Libraries</topic><topic>Linear programming</topic><topic>reinforcement learning</topic><topic>Road tests</topic><topic>Safety</topic><topic>Testing</topic><topic>testing scenario library</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Shuo</creatorcontrib><creatorcontrib>Feng, Yiheng</creatorcontrib><creatorcontrib>Sun, Haowei</creatorcontrib><creatorcontrib>Bao, Shan</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Liu, Henry X.</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>Feng, Shuo</au><au>Feng, Yiheng</au><au>Sun, Haowei</au><au>Bao, Shan</au><au>Zhang, Yi</au><au>Liu, Henry X.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>22</volume><issue>9</issue><spage>5635</spage><epage>5647</epage><pages>5635-5647</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2020.2988309</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5656-3222</orcidid><orcidid>https://orcid.org/0000-0002-2117-4427</orcidid><orcidid>https://orcid.org/0000-0001-5526-866X</orcidid><orcidid>https://orcid.org/0000-0002-3685-9920</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1524-9050 |
| ispartof | IEEE transactions on intelligent transportation systems, 2021-09, Vol.22 (9), p.5635-5647 |
| issn | 1524-9050 1558-0016 |
| language | eng |
| recordid | cdi_ieee_primary_9086089 |
| source | IEEE Electronic Library (IEL) |
| subjects | Automation Car following Connected and automated vehicles Estimation Frequency measurement functionality Indexes Libraries Linear programming reinforcement learning Road tests Safety Testing testing scenario library |
| title | Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T15%3A14%3A39IST&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=Testing%20Scenario%20Library%20Generation%20for%20Connected%20and%20Automated%20Vehicles,%20Part%20II:%20Case%20Studies&rft.jtitle=IEEE%20transactions%20on%20intelligent%20transportation%20systems&rft.au=Feng,%20Shuo&rft.date=2021-09-01&rft.volume=22&rft.issue=9&rft.spage=5635&rft.epage=5647&rft.pages=5635-5647&rft.issn=1524-9050&rft.eissn=1558-0016&rft.coden=ITISFG&rft_id=info:doi/10.1109/TITS.2020.2988309&rft_dat=%3Cproquest_RIE%3E2568070289%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=2568070289&rft_id=info:pmid/&rft_ieee_id=9086089&rfr_iscdi=true |