{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:56:32Z","timestamp":1783526192760,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shenzhen Municipal Science and Technology R&D Funding Basic Research Program","award":["JCYJ20210324133607021"],"award-info":[{"award-number":["JCYJ20210324133607021"]}]},{"name":"Municipal Government of Quzhou","award":["2022D037"],"award-info":[{"award-number":["2022D037"]}]},{"name":"Key Laboratory of Data Intelligence and Cognitive Computing, Longhua District, Shenzhen"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972069, 61836007, 61832017, 62272086"],"award-info":[{"award-number":["61972069, 61836007, 61832017, 62272086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614831","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"535-544","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross-Scenario Maneuver Decision with Adaptive Perception for Autonomous Driving"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9548-5069","authenticated-orcid":false,"given":"Yuan","family":"Fu","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3557-6598","authenticated-orcid":false,"given":"Shuncheng","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4492-8137","authenticated-orcid":false,"given":"Yuyang","family":"Xia","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2401-6499","authenticated-orcid":false,"given":"Fangda","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-3998","authenticated-orcid":false,"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Davide Del Testa","author":"Bojarski Mariusz","year":"2016","unstructured":"Mariusz Bojarski , Davide Del Testa , Daniel Dworakowski, Bernhard Firner , Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. 2016 . End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016). Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. 2016. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)."},{"key":"e_1_3_2_1_2_1","volume-title":"Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911","author":"Bojarski Mariusz","year":"2017","unstructured":"Mariusz Bojarski , Philip Yeres , Anna Choromanska , Krzysztof Choromanski , Bernhard Firner , Lawrence Jackel , and Urs Muller . 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911 ( 2017 ). Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller. 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911 (2017)."},{"key":"e_1_3_2_1_3_1","volume-title":"Openai gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman , Vicki Cheung , Ludwig Pettersson , Jonas Schneider , John Schulman , Jie Tang , and Wojciech Zaremba . 2016. Openai gym. arXiv preprint arXiv:1606.01540 ( 2016 ). Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_3_2_1_4_1","volume-title":"Model-free deep reinforcement learning for urban autonomous driving. In 2019 IEEE intelligent transportation systems conference (ITSC)","author":"Chen Jianyu","unstructured":"Jianyu Chen , Bodi Yuan , and Masayoshi Tomizuka . 2019. Model-free deep reinforcement learning for urban autonomous driving. In 2019 IEEE intelligent transportation systems conference (ITSC) . IEEE , 2765--2771. Jianyu Chen, Bodi Yuan, and Masayoshi Tomizuka. 2019. Model-free deep reinforcement learning for urban autonomous driving. In 2019 IEEE intelligent transportation systems conference (ITSC). IEEE, 2765--2771."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-15024-6_7"},{"key":"e_1_3_2_1_6_1","volume-title":"Traffic safety and the driver","author":"Evans Leonard","unstructured":"Leonard Evans . 1991. Traffic safety and the driver . Science Serving Society . Leonard Evans. 1991. Traffic safety and the driver. Science Serving Society."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_8_1","volume-title":"International conference on learning representations.","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins , Loic Matthey , Arka Pal , Christopher Burgess , Xavier Glorot , Matthew Botvinick , Shakir Mohamed , and Alexander Lerchner . 2017 . beta-vae: Learning basic visual concepts with a constrained variational framework . In International conference on learning representations. Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-vae: Learning basic visual concepts with a constrained variational framework. In International conference on learning representations."},{"key":"e_1_3_2_1_9_1","volume-title":"Combining planning and deep reinforcement learning in tactical decision making for autonomous driving","author":"Hoel Carl-Johan","year":"2019","unstructured":"Carl-Johan Hoel , Katherine Driggs-Campbell , Krister Wolff , Leo Laine , and Mykel J Kochenderfer . 2019. Combining planning and deep reinforcement learning in tactical decision making for autonomous driving . IEEE transactions on intelligent vehicles, Vol. 5 , 2 ( 2019 ), 294--305. Carl-Johan Hoel, Katherine Driggs-Campbell, Krister Wolff, Leo Laine, and Mykel J Kochenderfer. 2019. Combining planning and deep reinforcement learning in tactical decision making for autonomous driving. IEEE transactions on intelligent vehicles, Vol. 5, 2 (2019), 294--305."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_49"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/25.669106"},{"key":"e_1_3_2_1_14_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_15_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054625"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.55.5597"},{"key":"e_1_3_2_1_18_1","unstructured":"Edouard Leurent. 2018. An Environment for Autonomous Driving Decision-Making. https:\/\/github.com\/eleurent\/highway-env.  Edouard Leurent. 2018. An Environment for Autonomous Driving Decision-Making. https:\/\/github.com\/eleurent\/highway-env."},{"key":"e_1_3_2_1_19_1","volume-title":"Social attention for autonomous decision-making in dense traffic. arXiv preprint arXiv:1911.12250","author":"Leurent Edouard","year":"2019","unstructured":"Edouard Leurent and Jean Mercat . 2019. Social attention for autonomous decision-making in dense traffic. arXiv preprint arXiv:1911.12250 ( 2019 ). Edouard Leurent and Jean Mercat. 2019. Social attention for autonomous decision-making in dense traffic. arXiv preprint arXiv:1911.12250 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971","author":"Lillicrap Timothy P","year":"2015","unstructured":"Timothy P Lillicrap , Jonathan J Hunt , Alexander Pritzel , Nicolas Heess , Tom Erez , Yuval Tassa , David Silver , and Daan Wierstra . 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 ( 2015 ). Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)."},{"key":"e_1_3_2_1_21_1","volume-title":"Vision-Based Environmental Perception for Autonomous Driving. arXiv preprint arXiv:2212.11453","author":"Liu Fei","year":"2022","unstructured":"Fei Liu , Zihao Lu , and Xianke Lin . 2022a. Vision-Based Environmental Perception for Autonomous Driving. arXiv preprint arXiv:2212.11453 ( 2022 ). Fei Liu, Zihao Lu, and Xianke Lin. 2022a. Vision-Based Environmental Perception for Autonomous Driving. arXiv preprint arXiv:2212.11453 (2022)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467072"},{"key":"e_1_3_2_1_23_1","volume-title":"BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. arXiv preprint arXiv:2205.13542","author":"Liu Zhijian","year":"2022","unstructured":"Zhijian Liu , Haotian Tang , Alexander Amini , Xinyu Yang , Huizi Mao , Daniela Rus , and Song Han . 2022b. BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. arXiv preprint arXiv:2205.13542 ( 2022 ). Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela Rus, and Song Han. 2022b. BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation. arXiv preprint arXiv:2205.13542 (2022)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.09.001"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.58.1425"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.114030"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19030648"},{"key":"e_1_3_2_1_28_1","unstructured":"David Schrank Tim Lomax Bill Eisele etal 2011. 2011 urban mobility report. (2011).  David Schrank Tim Lomax Bill Eisele et al. 2011. 2011 urban mobility report. (2011)."},{"key":"e_1_3_2_1_29_1","volume-title":"High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438","author":"Schulman John","year":"2015","unstructured":"John Schulman , Philipp Moritz , Sergey Levine , Michael Jordan , and Pieter Abbeel . 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 ( 2015 ). John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015)."},{"key":"e_1_3_2_1_30_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman , Filip Wolski , Prafulla Dhariwal , Alec Radford , and Oleg Klimov . 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 ( 2017 ). John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving","author":"Teng Siyu","year":"2022","unstructured":"Siyu Teng , Long Chen , Yunfeng Ai , Yuanye Zhou , Zhe Xuanyuan , and Xuemin Hu. 2022. Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving . IEEE Transactions on Intelligent Vehicles ( 2022 ). Siyu Teng, Long Chen, Yunfeng Ai, Yuanye Zhou, Zhe Xuanyuan, and Xuemin Hu. 2022. Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving. IEEE Transactions on Intelligent Vehicles (2022)."},{"key":"e_1_3_2_1_32_1","volume-title":"Congested traffic states in empirical observations and microscopic simulations. Physical review E","author":"Treiber Martin","year":"2000","unstructured":"Martin Treiber , Ansgar Hennecke , and Dirk Helbing . 2000. Congested traffic states in empirical observations and microscopic simulations. Physical review E , Vol. 62 , 2 ( 2000 ), 1805. Martin Treiber, Ansgar Hennecke, and Dirk Helbing. 2000. Congested traffic states in empirical observations and microscopic simulations. Physical review E, Vol. 62, 2 (2000), 1805."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2010.5509799"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557435"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.3141\/2623-01"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482283"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575379"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5772\/51314"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102662"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614831","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614831","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:57Z","timestamp":1750178217000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":39,"alternative-id":["10.1145\/3583780.3614831","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614831","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}