{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T04:45:10Z","timestamp":1682570710780},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Life Robotics"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s10015-023-00863-1","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T07:02:50Z","timestamp":1679382170000},"page":"343-351","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Supervised vehicle trajectory prediction using orthogonal image map for urban automated driving"],"prefix":"10.1007","volume":"28","author":[{"given":"Keisuke","family":"Yoneda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amane","family":"Kinoshita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuke","family":"Takahashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadashi","family":"Okuno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Suganuma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"863_CR1","doi-asserted-by":"crossref","unstructured":"Franke U, Pfeiffer D, Rabe C, Knoeppel C, Enzweiler M, Stein F, Herrtwich RG (2013) Making bertha see. In: Proceedings of ICCV Workshop on Computer Vision for Autonomous Driving","DOI":"10.1109\/ICCVW.2013.36"},{"issue":"6","key":"863_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MM.2015.133","volume":"35","author":"S Kato","year":"2015","unstructured":"Kato S, Takeuchi E, Ishiguro Y, Ninomiya Y, Takeda K, Hamada T (2015) An open approach to autonomous vehicles. IEEE Micro 35(6):60\u201369","journal-title":"IEEE Micro"},{"key":"863_CR3","doi-asserted-by":"crossref","unstructured":"Werlind M, Ziegler J, Kammel S, Thrun S (2010) Optimal trajectory generation for dynamic street scenarios in a frenet frame. In: Proceedings of International Conference on Robotics and Automation, pp 987\u2013993","DOI":"10.1109\/ROBOT.2010.5509799"},{"key":"863_CR4","unstructured":"Tehrani H, Shimizu M, Ogawa T (2013) Adaptive lane change and lane keeping for safe and comfortable driving. In: Proceedings of Second International Symposium on Future Active Safety Technology Toward Zero-Traffic Accident"},{"key":"863_CR5","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/s10015-018-0484-4","volume":"23","author":"K Yoneda","year":"2018","unstructured":"Yoneda K, Iida T, Kim TH, Yanase R, Aldibaja M, Suganuma N (2018) Trajectory optimization and state selection for urban automated driving. Artif Life Robot 23:474\u2013480","journal-title":"Artif Life Robot"},{"issue":"2","key":"863_CR6","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TITS.2019.2962338","volume":"22","author":"S Kuuti","year":"2020","unstructured":"Kuuti S, Bowden R, Jin Y, Barber P, Fallah S (2020) A survey of deep learning applications to autonomous vehicle control. IEEE Trans Intell Transp Syst 22(2):712\u2013733","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"6","key":"863_CR7","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TITS.2021.3054625","volume":"23","author":"BR Kiran","year":"2021","unstructured":"Kiran BR, Sobh I, Talpaert V, Mannion P et al (2021) Deep reinforcement learning for autonomous driving: a survey. IEEE Trans Intell Transp Syst 23(6):4909\u20134926","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"863_CR8","unstructured":"Shah M, Huang Z, Laddha A, et al (2020) LiRaNet: end-to-end trajectory prediction using spatio-temporal radar fusion. In: Proceedings of 4th Conference on Robot Learning"},{"key":"863_CR9","doi-asserted-by":"crossref","unstructured":"Schulz J, Hubmann C, Morin N et al (2019) Learning interaction-aware probabilistic driver behavior models from urban scenarios. In: Proceedings of 2019 IEEE Intelligent Vehicles Symposium, pp 1326\u20131333","DOI":"10.1109\/IVS.2019.8814080"},{"key":"863_CR10","doi-asserted-by":"crossref","unstructured":"Choi C, Choi JH, Li J, Malla S (2021) Shared cross-modal trajectory prediction for autonomous driving. In: Proceedings of 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR46437.2021.00031"},{"key":"863_CR11","unstructured":"Bojarski M, Testa DD, Dworakowski D et al (2017) End to end learning for self-driving cars. arXiv preprint arXiv:1604:07316"},{"issue":"4","key":"863_CR12","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1109\/TIV.2018.2874555","volume":"3","author":"V John","year":"2018","unstructured":"John V, Boyali A, Tehrani H, Ishimaru K, Konishi M, Liu Z, Mita S (2018) Estimation of steering angle and collision avoidance for automated driving using deep mixture of experts. IEEE Trans Intell Veh 3(4):571\u2013584","journal-title":"IEEE Trans Intell Veh"},{"key":"863_CR13","doi-asserted-by":"crossref","unstructured":"Bansal M, Krizhevsky A, Ogale A (2018) ChauffeurNet: learning to drive by imitating the best and synthesizing the worst. arXiv preprint arXiv:1812:03079","DOI":"10.15607\/RSS.2019.XV.031"},{"key":"863_CR14","doi-asserted-by":"crossref","unstructured":"Chen J, Yuan B, Tomizuka M (2019) Model-free deep reinforcement learning for urban autonomous driving. In: Proceedings of 2019 IEEE Intelligent Transaction Systems Conference","DOI":"10.1109\/ITSC.2019.8917306"},{"key":"863_CR15","doi-asserted-by":"crossref","unstructured":"Codevilla F, Santana E, Lopez AM, Gaidon A (2019) Exploring the limitations of behavior cloning for autonomous driving. In: Proceedings of 2019 IEEE\/CVF International Conference on Computer Vision","DOI":"10.1109\/ICCV.2019.00942"},{"key":"863_CR16","doi-asserted-by":"crossref","unstructured":"Yoneda K, Hashimoto N, Yanase R, Aldibaja M, Suganuma N (2018) Vehicle localization using 76ghz omnidirectional millimeter-wave radar for winter automated driving. In: Proceedings of 2018 IEEE Intelligent Vehicles Symposium, pp 971\u2013977","DOI":"10.1109\/IVS.2018.8500378"},{"issue":"9","key":"863_CR17","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.3390\/s22093545","volume":"22","author":"R Yanase","year":"2022","unstructured":"Yanase R, Hirano D, Aldibaja M, Yoneda K, Suganuma N (2022) LiDAR- and radar-based robust vehicle localization with confidence estimation of matching results. Sensors 22(9):3545","journal-title":"Sensors"},{"key":"863_CR18","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations"}],"container-title":["Artificial Life and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-023-00863-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10015-023-00863-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-023-00863-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T17:09:07Z","timestamp":1682528947000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10015-023-00863-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":18,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["863"],"URL":"https:\/\/doi.org\/10.1007\/s10015-023-00863-1","relation":{},"ISSN":["1433-5298","1614-7456"],"issn-type":[{"value":"1433-5298","type":"print"},{"value":"1614-7456","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]},"assertion":[{"value":"14 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}