{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T10:37:59Z","timestamp":1777027079605,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Honda Research Institute as Research Interns, through the support of Honda Research Institute\u2019s resources and funding"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tits.2024.3424667","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T18:40:55Z","timestamp":1721068855000},"page":"10617-10635","source":"Crossref","is-referenced-by-count":9,"title":["Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7536-9983","authenticated-orcid":false,"given":"Chen","family":"Tang","sequence":"first","affiliation":[{"name":"Honda Research Institute, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nishan","family":"Srishankar","sequence":"additional","affiliation":[{"name":"Honda Research Institute, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujitha","family":"Martin","sequence":"additional","affiliation":[{"name":"Honda Research Institute, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0206-6639","authenticated-orcid":false,"given":"Masayoshi","family":"Tomizuka","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"End to end learning for self-driving cars","author":"Bojarski","year":"2016","journal-title":"arXiv:1604.07316"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2951362"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.320"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461053"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460504"},{"key":"ref9","first-page":"2701","article-title":"VAIN: Attentional multi-agent predictive modeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Hoshen"},{"key":"ref10","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Veli\u010dkovi\u0107"},{"key":"ref11","first-page":"2244","article-title":"Learning multiagent communication with backpropagation","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Sukhbaatar"},{"key":"ref12","first-page":"2688","article-title":"Neural relational inference for interacting systems","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Kipf"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_35"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569453"},{"key":"ref15","article-title":"Joint interaction and trajectory prediction for autonomous driving using graph neural networks","author":"Lee","year":"2019","journal-title":"arXiv:1912.07882"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196795"},{"key":"ref17","article-title":"Relational neural expectation maximization: Unsupervised discovery of objects and their interactions","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Van Steenkiste"},{"key":"ref18","first-page":"4502","article-title":"Interaction networks for learning about objects, relations and physics","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Battaglia"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561595"},{"key":"ref21","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv:1709.04875"},{"key":"ref22","first-page":"4565","article-title":"Generative adversarial imitation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Ho"},{"key":"ref23","article-title":"A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models","author":"Finn","year":"2016","journal-title":"arXiv:1611.03852"},{"key":"ref24","article-title":"Multi-agent adversarial inverse reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Yu"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593758"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793750"},{"key":"ref27","first-page":"5320","article-title":"Robust imitation of diverse behaviors","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Wang"},{"key":"ref28","article-title":"Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Peng"},{"key":"ref29","first-page":"11772","article-title":"Meta-inverse reinforcement learning with probabilistic context variables","author":"Yu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst. (NIPS)"},{"key":"ref30","first-page":"11698","article-title":"Causal confusion in imitation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"de Haan"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341072"},{"key":"ref32","first-page":"1433","article-title":"Maximum entropy inverse reinforcement learning","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Ziebart"},{"key":"ref33","first-page":"49","article-title":"Guided cost learning: Deep inverse optimal control via policy optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref34","article-title":"Learning robust rewards with adversarial inverse reinforcement learning","author":"Fu","year":"2017","journal-title":"arXiv:1710.11248"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2010.0084"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3227738"},{"key":"ref37","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th Int. Conf. Mach. Learning","volume":"70","author":"Gilmer"},{"key":"ref38","article-title":"Deep variational information bottleneck","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Alemi"},{"key":"ref39","article-title":"\u03b2-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Higgins"},{"key":"ref40","article-title":"Reinforcement learning and control as probabilistic inference: Tutorial and review","author":"Levine","year":"2018","journal-title":"arXiv:1805.00909"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814167"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.62.1805"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/S0001-4575(00)00019-1"},{"key":"ref44","first-page":"8481","article-title":"Exploring social posterior collapse in variational autoencoder for interaction modeling","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Tang"},{"key":"ref45","first-page":"3145","article-title":"Can autonomous vehicles identify, recover from, and adapt to distribution shifts?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Filos"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2021.XVII.037"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00643"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561967"},{"key":"ref49","first-page":"1457","article-title":"JFP: Joint future prediction with interactive multi-agent modeling for autonomous driving","volume-title":"Proc. Conf. Robot Learn.","author":"Luo"},{"key":"ref50","first-page":"19783","article-title":"Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning","volume-title":"Proc. NeurIPS","author":"Li"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6979\/10659279\/10598828.pdf?arnumber=10598828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T02:44:41Z","timestamp":1733885081000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10598828\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":50,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tits.2024.3424667","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}