{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:59:26Z","timestamp":1743155966206,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819980758"},{"type":"electronic","value":"9789819980765"}],"license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8076-5_22","type":"book-chapter","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T14:02:10Z","timestamp":1699884130000},"page":"303-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PnP: Integrated Prediction and\u00a0Planning for\u00a0Interactive Lane Change in\u00a0Dense Traffic"],"prefix":"10.1007","author":[{"given":"Xueyi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qichao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinfeng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongpu","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","unstructured":"Chauhan, N.S., Kumar, N.: Traffic flow forecasting using attention enabled Bi-LSTM and GRU hybrid model. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing, ICONIP 2022. CCIS, vol. 1794, pp. 505\u2013517. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-1648-1_42","DOI":"10.1007\/978-981-99-1648-1_42"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C., Hu, S., Nikdel, P., Mori, G., Savva, M.: Relational graph learning for crowd navigation. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10007\u201310013. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9340705"},{"key":"22_CR3","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Conference on Robot Learning, pp. 1\u201316. PMLR (2017)"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Gu, J., Sun, C., Zhao, H.: DenseTNT: end-to-end trajectory prediction from dense goal sets. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15303\u201315312 (2021)","DOI":"10.1109\/ICCV48922.2021.01502"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, Q., Wang, J., Liu, S.: Hierarchical reinforcement learning-based policy switching towards multi-scenarios autonomous driving. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534349"},{"key":"22_CR6","unstructured":"Hafner, D., Lillicrap, T., Ba, J., Norouzi, M.: Dream to control: Learning behaviors by latent imagination. In: International Conference on Learning Representations (2019)"},{"key":"22_CR7","unstructured":"Hafner, D., Lillicrap, T.P., Norouzi, M., Ba, J.: Mastering Atari with discrete world models. In: International Conference on Learning Representations (2020)"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Hagedorn, S., Hallgarten, M., Stoll, M., Condurache, A.: Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review. arXiv preprint arXiv:2308.05731 (2023)","DOI":"10.1109\/TIV.2024.3459071"},{"issue":"2","key":"22_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MCI.2019.2901089","volume":"14","author":"D Li","year":"2019","unstructured":"Li, D., Zhao, D., Zhang, Q., Chen, Y.: Reinforcement learning and deep learning based lateral control for autonomous driving [application notes]. IEEE Comput. Intell. Mag. 14(2), 83\u201398 (2019)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J., Zeng, W., Urtasun, R., Yumer, E.: Deep structured reactive planning. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 4897\u20134904. IEEE (2021)","DOI":"10.1109\/ICRA48506.2021.9561123"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gao, Y., Zhang, Q., Ding, D., Zhao, D.: Multi-task safe reinforcement learning for navigating intersections in dense traffic. J. Franklin Inst. (2022)","DOI":"10.1016\/j.jfranklin.2022.06.052"},{"issue":"7540","key":"22_CR12","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529\u2013533 (2015)","journal-title":"Nature"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Palmal, S., Arya, N., Saha, S., Tripathy, S.: A multi-modal graph convolutional network for predicting human breast cancer prognosis. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing, ICONIP 2022. Communications in Computer and Information Science, vol. 1794, pp. 187\u2013198. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-99-1648-1_16","DOI":"10.1007\/978-981-99-1648-1_16"},{"issue":"7587","key":"22_CR14","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10994-012-5280-0","volume":"87","author":"D Silver","year":"2012","unstructured":"Silver, D., Sutton, R.S., M\u00fcller, M.: Temporal-difference search in computer go. Mach. Learn. 87, 183\u2013219 (2012)","journal-title":"Mach. Learn."},{"issue":"3","key":"22_CR16","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/JAS.2021.1004395","volume":"9","author":"J Wang","year":"2021","unstructured":"Wang, J., Zhang, Q., Zhao, D.: Highway lane change decision-making via attention-based deep reinforcement learning. IEEE\/CAA J. Automatica Sinica 9(3), 567\u2013569 (2021)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"22_CR17","unstructured":"Wang, J., Zhang, Q., Zhao, D.: Dynamic-horizon model-based value estimation with latent imagination. IEEE Trans. Neural Netw. Learn. Syst. (2022)"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, Q., Zhao, D., Chen, Y.: Lane change decision-making through deep reinforcement learning with rule-based constraints. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852110"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Wen, J., Zhao, Z., Cui, J., Chen, B.M.: Model-based reinforcement learning with self-attention mechanism for autonomous driving in dense traffic. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds.) Neural Information Processing, ICONIP 2022. LNCS, vol. 13624, pp. 317\u2013330. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-30108-7_27","DOI":"10.1007\/978-3-031-30108-7_27"},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1109\/TIV.2022.3185159","volume":"8","author":"J Wu","year":"2022","unstructured":"Wu, J., Huang, Z., Lv, C.: Uncertainty-aware model-based reinforcement learning: methodology and application in autonomous driving. IEEE Trans. Intell. Veh. 8, 194\u2013203 (2022)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"4","key":"22_CR21","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/JAS.2020.1003228","volume":"7","author":"X Zhao","year":"2020","unstructured":"Zhao, X., Chen, Y., Guo, J., Zhao, D.: A spatial-temporal attention model for human trajectory prediction. IEEE CAA J. Autom. Sinica 7(4), 965\u2013974 (2020)","journal-title":"IEEE CAA J. Autom. Sinica"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8076-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T22:30:07Z","timestamp":1730500207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8076-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"ISBN":["9789819980758","9789819980765"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8076-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,14]]},"assertion":[{"value":"14 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}