Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, modeling complex interaction dynamics and capturing the possibility of many possible outcomes in such inte...
Uloženo v:
| Vydáno v: | IEEE robotics and automation letters Ročník 6; číslo 2; s. 295 - 302 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2377-3766, 2377-3766 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, modeling complex interaction dynamics and capturing the possibility of many possible outcomes in such interactive settings is very challenging, which has recently prompted the study of several different approaches. In this work, we provide a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability distribution over future human trajectories conditioned on past interactions and candidate robot future actions. Specifically, the goals of this tutorial paper are to review and build a taxonomy of state-of-the-art methods in human behavior prediction, from physics-based to purely data-driven methods, provide a rigorous yet easily accessible description of a data-driven, CVAE-based approach, highlight important design characteristics that make this an attractive model to use in the context of model-based planning for human-robot interactions, and provide important design considerations when using this class of models. |
|---|---|
| AbstractList | Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, modeling complex interaction dynamics and capturing the possibility of many possible outcomes in such interactive settings is very challenging, which has recently prompted the study of several different approaches. In this work, we provide a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability distribution over future human trajectories conditioned on past interactions and candidate robot future actions. Specifically, the goals of this tutorial paper are to review and build a taxonomy of state-of-the-art methods in human behavior prediction, from physics-based to purely data-driven methods, provide a rigorous yet easily accessible description of a data-driven, CVAE-based approach, highlight important design characteristics that make this an attractive model to use in the context of model-based planning for human-robot interactions, and provide important design considerations when using this class of models. |
| Author | Leung, Karen Schmerling, Edward Ivanovic, Boris Pavone, Marco |
| Author_xml | – sequence: 1 givenname: Boris orcidid: 0000-0002-8698-202X surname: Ivanovic fullname: Ivanovic, Boris email: borisi@stanford.edu organization: Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA – sequence: 2 givenname: Karen orcidid: 0000-0002-3033-8761 surname: Leung fullname: Leung, Karen email: karen.ym.leung@gmail.com organization: Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA – sequence: 3 givenname: Edward surname: Schmerling fullname: Schmerling, Edward email: ednerd@gmail.com organization: Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, USA – sequence: 4 givenname: Marco orcidid: 0000-0002-0206-4337 surname: Pavone fullname: Pavone, Marco email: pavone@stanford.edu organization: Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA |
| BookMark | eNp9kEFLAzEQhYNUsNbeBS8Bz62TZJvseluqVqFFkep1SZMppm43NZsV-u_d2iLiwdPMwHzvzbxT0ql8hYScMxgyBtnV9DkfcuAwFJAIJsUR6XKh1EAoKTu_-hPSr-sVALARVyIbdcn7rCmjW3urS3qDuKETrDDo6D6RzrzFsqZLH-g86BWa6MOWPgW0zkTnq2ua07GvrNsNLf-qg9OHPm-ix8q0CoHmm03w2rydkeOlLmvsH2qPvNzdzsf3g-nj5GGcTweGZywOtE7AZiyVI52xhVSW4YKnBsFIawRou1QqMwmaxUgxhlokCw0MEmUNoFRG9MjlXre1_WiwjsXKN6G9qi54orhgKSSs3YL9lgm-rgMui01wax22BYNil2rRplrsUi0OqbaI_IMYF78_jkG78j_wYg86RPzxyXgqk5SLL5azh0Q |
| CODEN | IRALC6 |
| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2024_120651 crossref_primary_10_3390_sym16010118 crossref_primary_10_1109_TITS_2022_3207347 crossref_primary_10_3390_aerospace10040357 crossref_primary_10_1016_j_inffus_2025_103588 crossref_primary_10_1109_TKDE_2022_3185115 crossref_primary_10_1007_s11768_023_00170_x crossref_primary_10_1109_TBDATA_2023_3310241 crossref_primary_10_1109_LRA_2022_3156856 crossref_primary_10_1109_ACCESS_2024_3524906 crossref_primary_10_1016_j_rcim_2021_102304 crossref_primary_10_1109_LRA_2023_3312035 crossref_primary_10_1016_j_compbiomed_2022_105403 crossref_primary_10_1080_01691864_2022_2035253 crossref_primary_10_1109_OJITS_2025_3580271 crossref_primary_10_1002_eer2_70006 crossref_primary_10_1016_j_engappai_2024_109225 crossref_primary_10_1016_j_buildenv_2021_108457 crossref_primary_10_2514_1_I011545 crossref_primary_10_1016_j_trc_2021_103114 crossref_primary_10_1109_TITS_2024_3419037 crossref_primary_10_1109_TNNLS_2025_3550350 crossref_primary_10_32604_cmc_2024_056222 crossref_primary_10_1109_TII_2023_3302304 crossref_primary_10_1109_TITS_2022_3205676 crossref_primary_10_1109_ACCESS_2021_3116303 crossref_primary_10_1080_23249935_2024_2407076 crossref_primary_10_1109_TPWRS_2021_3107515 crossref_primary_10_1016_j_eswa_2024_125708 crossref_primary_10_1109_TCSVT_2022_3232112 crossref_primary_10_3390_app142210350 crossref_primary_10_1016_j_trc_2022_103829 crossref_primary_10_1016_j_measurement_2022_111409 crossref_primary_10_1177_0954407021997667 crossref_primary_10_1016_j_ifacol_2025_07_021 crossref_primary_10_1002_aisy_202300359 crossref_primary_10_1109_TSM_2022_3146266 crossref_primary_10_1016_j_commtr_2025_100166 crossref_primary_10_1109_TVT_2024_3349601 crossref_primary_10_1145_3719290 crossref_primary_10_1016_j_sigpro_2023_109165 crossref_primary_10_3390_electronics10212654 crossref_primary_10_18267_j_aip_235 crossref_primary_10_1016_j_chaos_2024_115604 crossref_primary_10_3390_s21124248 crossref_primary_10_1007_s10489_025_06805_7 crossref_primary_10_1109_LRA_2022_3231531 |
| Cites_doi | 10.1109/CVPR.2017.233 10.1038/35035023 10.1103/PhysRevE.62.1805 10.1109/IVS.2015.7225830 10.1109/CVPR42600.2020.01164 10.1145/1015330.1015430 10.1109/IVS.2018.8500493 10.1177/0278364920917446 10.1177/0278364920950795 10.1145/2909824.3020253 10.1109/ICRA40945.2020.9196697 10.1162/neco.1997.9.8.1735 10.1109/HRI.2019.8673256 10.1103/PhysRevE.51.4282 10.15607/RSS.2016.XII.029 10.1007/978-3-642-59751-0_36 10.1109/CVPR.2016.573 10.1109/IROS.2018.8594393 10.1109/ICRA.2018.8460766 10.1111/j.1467-8659.2007.01089.x 10.1109/ICCV.2019.00246 10.1177/0278364915619772 10.15607/RSS.2019.XV.048 10.1109/CVPR.2018.00240 10.1109/CVPR.2016.110 10.1109/ICRA.2019.8794465 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/LRA.2020.3043163 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2377-3766 |
| EndPage | 302 |
| ExternalDocumentID | 10_1109_LRA_2020_3043163 9286482 |
| Genre | orig-research |
| GroupedDBID | 0R~ 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFS AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF KQ8 M43 M~E O9- OCL RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c291t-aa40d91865a91b67d1eb28ce0c6dc30adf779c4ecb5711ea34ba01047dc0e67c3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 87 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000602951000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2377-3766 |
| IngestDate | Mon Jun 30 03:56:42 EDT 2025 Sat Nov 29 06:03:09 EST 2025 Tue Nov 18 22:33:11 EST 2025 Wed Aug 27 02:32:33 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c291t-aa40d91865a91b67d1eb28ce0c6dc30adf779c4ecb5711ea34ba01047dc0e67c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-3033-8761 0000-0002-8698-202X 0000-0002-0206-4337 |
| PQID | 2472318041 |
| PQPubID | 4437225 |
| PageCount | 8 |
| ParticipantIDs | crossref_primary_10_1109_LRA_2020_3043163 crossref_citationtrail_10_1109_LRA_2020_3043163 proquest_journals_2472318041 ieee_primary_9286482 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-04-01 |
| PublicationDateYYYYMMDD | 2021-04-01 |
| PublicationDate_xml | – month: 04 year: 2021 text: 2021-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE robotics and automation letters |
| PublicationTitleAbbrev | LRA |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 narayanan (ref15) 0 ref14 ref31 ref30 ziebart (ref19) 0 ref11 ref10 pellegrini (ref43) 0 salzmann (ref8) 0 ref1 ref39 kosaraju (ref32) 0 ref16 ng (ref17) 0 ref18 salimans (ref33) 0 arjovsky (ref35) 0 sohn (ref2) 0 de haan (ref4) 0 lavalle (ref40) 0 sadeghian (ref42) 0 ref24 ref45 ref23 ref25 itkina (ref46) 0 ref20 ref41 ref22 ref44 ref21 adel (ref38) 0 kingma (ref28) 2013 ref29 ref7 ref9 ref3 ref6 ref5 goodfellow (ref26) 0 mirza (ref27) 2014 arjovsky (ref34) 0 maddison (ref37) 0 jang (ref36) 0 |
| References_xml | – ident: ref30 doi: 10.1109/CVPR.2017.233 – ident: ref11 doi: 10.1038/35035023 – year: 0 ident: ref46 article-title: Evidential sparsification of multimodal latent spaces in conditional variational autoencoders publication-title: Proc Conf Neural Inform Process Syst – ident: ref12 doi: 10.1103/PhysRevE.62.1805 – ident: ref39 doi: 10.1109/IVS.2015.7225830 – year: 2014 ident: ref27 – ident: ref45 doi: 10.1109/CVPR42600.2020.01164 – start-page: 261-268 year: 0 ident: ref43 article-title: You'll never walk alone: Modeling social behavior for multi-target tracking publication-title: IEEE Int Conf Comput Vis – ident: ref18 doi: 10.1145/1015330.1015430 – ident: ref31 doi: 10.1109/IVS.2018.8500493 – ident: ref1 doi: 10.1177/0278364920917446 – start-page: 11698 year: 0 ident: ref4 article-title: Causal confusion in imitation learning publication-title: Proc Conf Neural Inform Process Syst – ident: ref5 doi: 10.1177/0278364920950795 – ident: ref13 doi: 10.1145/2909824.3020253 – ident: ref41 doi: 10.1109/ICRA40945.2020.9196697 – start-page: 3483 year: 0 ident: ref2 article-title: Learning structured output representation using deep conditional generative models publication-title: Proc Conf Neural Inform Process Syst – ident: ref25 doi: 10.1162/neco.1997.9.8.1735 – ident: ref22 doi: 10.1109/HRI.2019.8673256 – ident: ref9 doi: 10.1103/PhysRevE.51.4282 – ident: ref20 doi: 10.15607/RSS.2016.XII.029 – start-page: 2234 year: 0 ident: ref33 article-title: Improved techniques for training GANs publication-title: Proc Conf Neural Inform Process Syst – ident: ref10 doi: 10.1007/978-3-642-59751-0_36 – ident: ref24 doi: 10.1109/CVPR.2016.573 – year: 0 ident: ref37 article-title: The concrete distribution: A continuous relaxation of discrete random variables publication-title: Proc Int Conf Learn Representations – start-page: 214 year: 0 ident: ref35 article-title: Wasserstein generative adversarial networks publication-title: Proc Int Conf Mach Learn – year: 0 ident: ref36 article-title: Categorial reparameterization with gumbel-softmax publication-title: Proc Int Conf Learn Representations – start-page: 137 year: 0 ident: ref32 article-title: Social-BiGAT: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks publication-title: Proc Conf Neural Inform Process Syst – year: 0 ident: ref15 article-title: ProxEmo: Gait-based emotion learning and multi-view proxemic fusion for socially-aware robot navigation publication-title: Proc IEEE/RSJ Int Conf Intell Robots Syst – year: 0 ident: ref42 article-title: CAR-Net: Clairvoyant attentive recurrent network publication-title: Proc Eur Conf Comput Vis – ident: ref6 doi: 10.1109/IROS.2018.8594393 – ident: ref3 doi: 10.1109/ICRA.2018.8460766 – ident: ref44 doi: 10.1111/j.1467-8659.2007.01089.x – ident: ref7 doi: 10.1109/ICCV.2019.00246 – ident: ref21 doi: 10.1177/0278364915619772 – start-page: 663 year: 0 ident: ref17 article-title: Algorithms for inverse reinforcement learning publication-title: Proc Int Conf Mach Learn – start-page: 683 year: 0 ident: ref8 article-title: Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data publication-title: Proc Eur Conf Comput Vis – start-page: 2672 year: 0 ident: ref26 article-title: Generative adversarial nets publication-title: Proc Conf Neural Inform Process Syst – ident: ref14 doi: 10.15607/RSS.2019.XV.048 – ident: ref29 doi: 10.1109/CVPR.2018.00240 – year: 0 ident: ref19 article-title: Maximum entropy inverse reinforcement learning publication-title: Proc AAAI Conf Artif Intell – year: 2013 ident: ref28 – ident: ref23 doi: 10.1109/CVPR.2016.110 – start-page: 50 year: 0 ident: ref38 article-title: Discovering interpretable representations for both deep generative and discriminative models publication-title: Proc Int Conf Mach Learn – year: 0 ident: ref34 article-title: Towards principled methods for training generative adversarial networks publication-title: Proc Int Conf Learn Representations – start-page: 743 year: 0 ident: ref40 article-title: Better unicycle models publication-title: Planning Algorithms – ident: ref16 doi: 10.1109/ICRA.2019.8794465 |
| SSID | ssj0001527395 |
| Score | 2.53962 |
| Snippet | Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 295 |
| SubjectTerms | Algorithms Autonomous vehicle navigation Context modeling Data models deep learning methods Human behavior Mathematical model Planning Prediction models Predictive models Robots social HRI Taxonomy Trajectory |
| Title | Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach |
| URI | https://ieeexplore.ieee.org/document/9286482 https://www.proquest.com/docview/2472318041 |
| Volume | 6 |
| WOSCitedRecordID | wos000602951000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2377-3766 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001527395 issn: 2377-3766 databaseCode: RIE dateStart: 20160101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2377-3766 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001527395 issn: 2377-3766 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH-44UEPfk1xOiUHL4J1_ciSxluZGx50DFHxVtLkCepcx9YJXvzbTbJuKorgLYcXKP217_v9HsBRRmOjGrnvSRNdeDRS5hRl0pOBVMgChg-hdMsmeK8X39-L_hKcLGZhENE1n-GpPbpavs7V1KbKmiKMGY2Nwq1wzmazWp_5FMskJlrzSqQvmpfXiYn_QhOWWgIZFn2zPG6Vyg_964xKd_1_j7MBa6XzSJIZ2puwhMMtWP1CKViDZzdR-5JrI3eOOCIzXmmr1IjdezaYEOOmEmOinly-_o30x7ZWY_E5Iwlp57aG7fKD5M7E0WWukCTTIreclxrHJCl5yLfhttu5aV945UIFT4UiKDwpqa9FELOWFEHGuA5MXB0r9BXTKvKlfuBcKIoqa_EgQBnRTDr-Hq18ZFxFO1Ad5kPcBSK5oixmxpaFgpqLmeA6i5BriS1hfLA6NOcvO1Ul27hdejFIXdThi9TAk1p40hKeOhwvboxmTBt_yNYsHAu5Eok6NOZ4puWvOElDyo0Pa2mW9n6_tQ8roW1Uce04DagW4ykewLJ6LR4n40OoXL13Dt239gG1g9OH |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JSwMxFH6ICurBXaxWzcGL4NhZ0mTibXChYi1FVLwNmeQJbp3SRfDfm6RpVRTBWw4vMMw38_b3PYD9gqZGNfIwkCa6CGiizCkpZCAjqZBFDB9i6ZZN8FYrvb8X7Sk4nMzCIKJrPsMje3S1fF2qoU2V1UScMpoahTtjN2f5aa3PjIrlEhP1cS0yFLXmdWYiwNgEppZChiXfbI9bpvJDAzuzcr70vwdahkXvPpJshPcKTGFnFRa-kAquwbObqX0ttZE7ReySEbO0VWvEbj576RPjqBJjpJ5cxv6dtHu2WmMROiYZOSltFdtlCMmdiaR9tpBkw0FpWS819kjmmcjX4fb87OakEfiVCoGKRTQIpKShFlHK6lJEBeM6MpF1qjBUTKsklPqBc6EoqqLOowhlQgvpGHy0CpFxlWzAdKfs4CYQyRVlKTPWLBbUXCwE10WCXEusC-OFVaA2ftm58nzjdu3FS-7ijlDkBp7cwpN7eCpwMLnRHXFt_CG7ZuGYyHkkKlAd45n7n7Gfx5QbL9YSLW39fmsP5ho3V828edG63Ib52LatuOacKkwPekPcgVn1Nnjs93bdF_cB3lrVnw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodal+Deep+Generative+Models+for+Trajectory+Prediction%3A+A+Conditional+Variational+Autoencoder+Approach&rft.jtitle=IEEE+robotics+and+automation+letters&rft.au=Ivanovic%2C+Boris&rft.au=Leung%2C+Karen&rft.au=Schmerling%2C+Edward&rft.au=Pavone%2C+Marco&rft.date=2021-04-01&rft.issn=2377-3766&rft.eissn=2377-3766&rft.volume=6&rft.issue=2&rft.spage=295&rft.epage=302&rft_id=info:doi/10.1109%2FLRA.2020.3043163&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_LRA_2020_3043163 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2377-3766&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2377-3766&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2377-3766&client=summon |