Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic
Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite sy...
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| Veröffentlicht in: | IEEE sensors journal 2021-10, Vol.21 (19), p.21675-21687 |
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| creator | Liu, Wei Xia, Xin Xiong, Lu Lu, Yishi Gao, Letian Yu, Zhuoping |
| description | Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios. |
| doi_str_mv | 10.1109/JSEN.2021.3059050 |
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In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2021.3059050</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Automated vehicle ; Automation ; Delays ; Dynamic control ; Engineering ; Engineering, Electrical & Electronic ; Estimation ; Global navigation satellite system ; Grey prediction ; Inertial platforms ; information fusion ; Instruments & Instrumentation ; Kalman filters ; Lane changing ; low sampling rate ; measurement signal delay ; Observers ; Physical Sciences ; Physics ; Physics, Applied ; Pitch (inclination) ; Prediction algorithms ; Rolling motion ; Science & Technology ; Sideslip ; Signal delay ; Signal measurement ; Smoothing methods ; Steering ; Technology ; Velocity errors ; Wheels</subject><ispartof>IEEE sensors journal, 2021-10, Vol.21 (19), p.21675-21687</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>125</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000702716000065</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c359t-a1081b6602b5fbade56d50d6e7b524d92422e8242cdd9dcec1ddd9f07a2c9c8b3</citedby><cites>FETCH-LOGICAL-c359t-a1081b6602b5fbade56d50d6e7b524d92422e8242cdd9dcec1ddd9f07a2c9c8b3</cites><orcidid>0000-0002-1673-2658 ; 0000-0002-8775-0052 ; 0000-0003-4251-5793 ; 0000-0002-5108-7578</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9556130$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27933,27934,39267,54767</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9556130$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Xia, Xin</creatorcontrib><creatorcontrib>Xiong, Lu</creatorcontrib><creatorcontrib>Lu, Yishi</creatorcontrib><creatorcontrib>Gao, Letian</creatorcontrib><creatorcontrib>Yu, Zhuoping</creatorcontrib><title>Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><addtitle>IEEE SENS J</addtitle><description>Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.</description><subject>Automated vehicle</subject><subject>Automation</subject><subject>Delays</subject><subject>Dynamic control</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Estimation</subject><subject>Global navigation satellite system</subject><subject>Grey prediction</subject><subject>Inertial platforms</subject><subject>information fusion</subject><subject>Instruments & Instrumentation</subject><subject>Kalman filters</subject><subject>Lane changing</subject><subject>low sampling rate</subject><subject>measurement signal delay</subject><subject>Observers</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics, Applied</subject><subject>Pitch (inclination)</subject><subject>Prediction algorithms</subject><subject>Rolling motion</subject><subject>Science & Technology</subject><subject>Sideslip</subject><subject>Signal delay</subject><subject>Signal measurement</subject><subject>Smoothing methods</subject><subject>Steering</subject><subject>Technology</subject><subject>Velocity errors</subject><subject>Wheels</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>HGBXW</sourceid><recordid>eNqNkM9PwyAUxxujiXP6BxgvTTyazgcdpRyXZv7K1MPU6Kmh8LqxbO0EGuN_L3VGr17gkff5POAbRacERoSAuLybTx9GFCgZpcAEMNiLBoSxPCF8nO_3dQrJOOWvh9GRcysAIjjjg-ht0vl2Iz3q-AWXRq0xnhuNbm228aRZhOPUeRMA0zZx0TYuNK1pFoFaNHId36N0ncUNNj4ultJK5UM_KOo4Oqjl2uHJzz6Mnq-mT8VNMnu8vi0ms0SlTPhEEshJlWVAK1ZXUiPLNAOdIa8YHWtBx5RiHlaltdAKFdGhqIFLqoTKq3QYne_mbm373qHz5artbHibKynjOQ23gAgU2VHKts5ZrMutDd-ynyWBsk-w7BMs-wTLnwSDc7FzPrBqa6cMNgp_PQDgQDnJQgEZC3T-f7ow_jvSou0aH9SznWoQ_xTBWEZSSL8AzKuO2A</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Liu, Wei</creator><creator>Xia, Xin</creator><creator>Xiong, Lu</creator><creator>Lu, Yishi</creator><creator>Gao, Letian</creator><creator>Yu, Zhuoping</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>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-1673-2658</orcidid><orcidid>https://orcid.org/0000-0002-8775-0052</orcidid><orcidid>https://orcid.org/0000-0003-4251-5793</orcidid><orcidid>https://orcid.org/0000-0002-5108-7578</orcidid></search><sort><creationdate>20211001</creationdate><title>Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic</title><author>Liu, Wei ; Xia, Xin ; Xiong, Lu ; Lu, Yishi ; Gao, Letian ; Yu, Zhuoping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-a1081b6602b5fbade56d50d6e7b524d92422e8242cdd9dcec1ddd9f07a2c9c8b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Automated vehicle</topic><topic>Automation</topic><topic>Delays</topic><topic>Dynamic control</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>Estimation</topic><topic>Global navigation satellite system</topic><topic>Grey prediction</topic><topic>Inertial platforms</topic><topic>information fusion</topic><topic>Instruments & Instrumentation</topic><topic>Kalman filters</topic><topic>Lane changing</topic><topic>low sampling rate</topic><topic>measurement signal delay</topic><topic>Observers</topic><topic>Physical Sciences</topic><topic>Physics</topic><topic>Physics, Applied</topic><topic>Pitch (inclination)</topic><topic>Prediction algorithms</topic><topic>Rolling motion</topic><topic>Science & Technology</topic><topic>Sideslip</topic><topic>Signal delay</topic><topic>Signal measurement</topic><topic>Smoothing methods</topic><topic>Steering</topic><topic>Technology</topic><topic>Velocity errors</topic><topic>Wheels</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Xia, Xin</creatorcontrib><creatorcontrib>Xiong, Lu</creatorcontrib><creatorcontrib>Lu, Yishi</creatorcontrib><creatorcontrib>Gao, Letian</creatorcontrib><creatorcontrib>Yu, Zhuoping</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>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Wei</au><au>Xia, Xin</au><au>Xiong, Lu</au><au>Lu, Yishi</au><au>Gao, Letian</au><au>Yu, Zhuoping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><stitle>IEEE SENS J</stitle><date>2021-10-01</date><risdate>2021</risdate><volume>21</volume><issue>19</issue><spage>21675</spage><epage>21687</epage><pages>21675-21687</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. 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| subjects | Automated vehicle Automation Delays Dynamic control Engineering Engineering, Electrical & Electronic Estimation Global navigation satellite system Grey prediction Inertial platforms information fusion Instruments & Instrumentation Kalman filters Lane changing low sampling rate measurement signal delay Observers Physical Sciences Physics Physics, Applied Pitch (inclination) Prediction algorithms Rolling motion Science & Technology Sideslip Signal delay Signal measurement Smoothing methods Steering Technology Velocity errors Wheels |
| title | Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic |
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