{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:14:41Z","timestamp":1782314081035,"version":"3.54.5"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/iv55152.2023.10186812","type":"proceedings-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T17:20:05Z","timestamp":1690478405000},"page":"1-8","source":"Crossref","is-referenced-by-count":8,"title":["Physics Constrained Motion Prediction with Uncertainty Quantification"],"prefix":"10.1109","author":[{"given":"Renukanandan","family":"Tumu","sequence":"first","affiliation":[{"name":"University of Pennsylvania,Philadelphia,PA,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Lindemann","sequence":"additional","affiliation":[{"name":"University of Southern California,Los Angeles,CA,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Truong","family":"Nghiem","sequence":"additional","affiliation":[{"name":"Northern Arizona University,Flagstaff,AZ,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Mangharam","sequence":"additional","affiliation":[{"name":"University of Pennsylvania,Philadelphia,PA,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref12","article-title":"Wayformer: Motion Forecasting via Simple & Efficient Attention Networks","author":"nayakanti","year":"2022"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2007.4282788"},{"key":"ref15","article-title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","author":"angelopoulos","year":"2022"},{"key":"ref14","article-title":"Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data","author":"salzmann","year":"2021"},{"key":"ref20","article-title":"Attention Is All You Need","author":"vaswani","year":"2017"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/978-3-030-58536-5_32","article-title":"Learning Lane Graph Representations for Motion Forecasting","author":"liang","year":"2020","journal-title":"Computer Vision &#x2013; ECCV 2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1080\/00423114.2019.1631455"},{"key":"ref10","article-title":"THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling","author":"gilles","year":"2022"},{"key":"ref21","first-page":"77","article-title":"F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning","author":"o\u2019kelly","year":"0"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01473"},{"key":"ref17","article-title":"Safe Planning in Dynamic Environments using Conformal Prediction","author":"lindemann","year":"2022"},{"key":"ref16","first-page":"6216","article-title":"Conformal Time-series Forecasting","volume":"34","author":"stankeviciute","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref19","article-title":"Conformalized Quantile Regression","author":"romano","year":"2019"},{"key":"ref18","article-title":"Adaptive Conformal Prediction for Motion Planning among Dynamic Agents","author":"dixit","year":"2022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995951"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0225-y"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"karniadakis","year":"2021","journal-title":"Nature Reviews Physics"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812107"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3156011"},{"key":"ref5","first-page":"263","article-title":"Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling","author":"djeumou","year":"0"}],"event":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","location":"Anchorage, AK, USA","start":{"date-parts":[[2023,6,4]]},"end":{"date-parts":[[2023,6,7]]}},"container-title":["2023 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10186382\/10186383\/10186812.pdf?arnumber=10186812","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T17:35:04Z","timestamp":1692034504000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10186812\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/iv55152.2023.10186812","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}