{"type":"ScholarlyArticle","title":"CDSTraj: Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving","authors":[{"name":"Haicheng Liao","slug":"haicheng-liao","given":"Haicheng","family":"Liao","affiliation":"University of Macau"},{"name":"Xuelin Li","slug":"xuelin-li","given":"Xuelin","family":"Li","affiliation":"University of Electronic Science and Technology of China {yc27979,"},{"name":"Yongkang Li","slug":"yongkang-li","given":"Yongkang","family":"Li","affiliation":"University of Electronic Science and Technology of China {yc27979,"},{"name":"Hanlin Kong","slug":"hanlin-kong","given":"Hanlin","family":"Kong","affiliation":"University of Electronic Science and Technology of China {yc27979,"},{"name":"Chengyue Wang","slug":"chengyue-wang","given":"Chengyue","family":"Wang","affiliation":"University of Macau"},{"name":"Bonan Wang","slug":"bonan-wang","given":"Bonan","family":"Wang","affiliation":"University of Macau"},{"name":"Yanchen Guan","slug":"yanchen-guan","given":"Yanchen","family":"Guan","affiliation":"University of Macau"},{"name":"KaHou Tam","slug":"kahou-tam","given":"KaHou","family":"Tam","affiliation":"University of Macau"},{"name":"Zhenning Li","slug":"zhenning-li","given":"Zhenning","family":"Li","affiliation":"University of Macau"}],"abstract":"Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model's unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.","doi":"10.24963/ijcai.2024/811","url":"https://ijcai.org/proceedings/2024/811","pdf":"https://ijcai.org/proceedings/2024/0811.pdf","markdown":"https://ijcai.org/proceedings/2024/811.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/811","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":811,"pages":{"first":7331,"last":7339},"keywords":["Planning and Scheduling: General","Robotics: General"],"videos":[],"references":[{"position":1,"raw":"[Alahi et al., 2016] Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Sil- vio Savarese. Social lstm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE CVPR, pages 961-971, 2016.","title":"Social lstm: Human trajectory prediction in crowded spaces","authors":"Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese","year":"2016","venue":"Proceedings of the IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Altché and de La Fortelle, 2017] Florent Altché and Ar- naud de La Fortelle. An lstm network for highway tra- jectory prediction. In IEEE 20th ITSC, pages 353-359. IEEE, 2017.","title":"Florent Altché and Arnaud de La Fortelle. An lstm network for highway trajectory prediction","authors":"Altché, La De, Fortelle","year":"2017","venue":"IEEE 20th ITSC","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2021] Yingfeng Cai, Zihao Wang, Hai Wang, Long Chen, Yicheng Li, Miguel Angel Sotelo, and Zhix- iong Li. Environment-attention network for vehicle trajec- tory prediction. IEEE Transactions on Vehicular Technol- ogy, 70(11):11216-11227, 2021.","title":"Environment-attention network for vehicle trajectory prediction","authors":"Yingfeng Cai, Zihao Wang, Hai Wang, Long Chen, Yicheng Li, Miguel Angel Sotelo, Zhixiong Li","year":"2021","venue":"IEEE Transactions on Vehicular Technology","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Xiaobo Chen, Huanjia Zhang, Feng Zhao, Yu Hu, Chenkai Tan, and Jian Yang. Intention- aware vehicle trajectory prediction based on spatial- temporal dynamic attention network for internet of vehi- cles. IEEE Transactions on Intelligent Transportation Sys- tems, 23(10):19471-19483, 2022.","title":"Intentionaware vehicle trajectory prediction based on spatialtemporal dynamic attention network for internet of vehicles","authors":"Xiaobo Chen, Huanjia Zhang, Feng Zhao, Yu Hu, Chenkai Tan, Jian Yang","year":"2022","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cong et al., 2023] Peichao Cong, Yixuan Xiao, Xianquan Wan, Murong Deng, Jiaxing Li, and Xin Zhang. Dacr- amtp: adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk. IEEE Transactions on Intelligent Vehicles, 2023.","title":"Dacramtp: adaptive multi-modal vehicle trajectory prediction for dynamic drivable areas based on collision risk","authors":"Cong","year":"2023","venue":"IEEE Transactions on Intelligent Vehicles","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deo and Trivedi, 2018a] Nachiket Deo and Mohan M Trivedi. Convolutional social pooling for vehicle trajectory prediction. In Proceedings of the IEEE CVPR Workshops, pages 1468-1476, 2018.","title":"Convolutional social pooling for vehicle trajectory prediction","authors":"Trivedi Deo, Nachiket Deo, Mohan M Trivedi","year":"2018","venue":"Proceedings of the IEEE CVPR Workshops","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Deo and Trivedi, 2018b] Nachiket Deo and Mohan M Trivedi. Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms. In IEEE IV, pages 1179-1184. IEEE, 2018.","title":"Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms","authors":"Trivedi Deo, Nachiket Deo, Mohan M Trivedi","year":"2018","venue":"IEEE IV","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gao et al., 2023] Kai Gao, Xunhao Li, Bin Chen, Lin Hu, Jian Liu, Ronghua Du, and Yongfu Li. Dual transformer based prediction for lane change intentions and trajecto- ries in mixed traffic environment. IEEE Transactions on Intelligent Transportation Systems, 2023.","title":"Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment","authors":"Kai Gao, Xunhao Li, Bin Chen, Lin Hu, Jian Liu, Ronghua Du, Yongfu Li","year":"2023","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gupta et al., 2018] Agrim Gupta, Justin Johnson, Li Fei- Fei, Silvio Savarese, and Alexandre Alahi. Social gan: Socially acceptable trajectories with generative adversar- ial networks. In Proceedings of the IEEE CVPR, pages 2255-2264, 2018.","title":"Social gan: Socially acceptable trajectories with generative adversarial networks","authors":"Gupta","year":"2018","venue":"Proceedings of the IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.","title":"Denoising diffusion probabilistic models","authors":"Jonathan Ho, Ajay Jain, Pieter Abbeel","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ho et al., 2022] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022.","title":"Imagen video: High definition video generation with diffusion models","authors":"Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, P Diederik, Ben Kingma, Mohammad Poole, David J Norouzi, Fleet","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu, 2020] Dichao Hu. An introductory survey on atten- tion mechanisms in nlp problems. In Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Sys- tems Conference (IntelliSys) Volume 2, pages 432-448. Springer, 2020.","title":"An introductory survey on attention mechanisms in nlp problems","authors":"Dichao Hu, Hu","year":"2020","venue":"Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2022] Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, and Hong Chen. A survey on trajectory-prediction methods for autonomous driving. IEEE Transactions on Intelligent Vehicles, 7(3):652-674, 2022.","title":"A survey on trajectory-prediction methods for autonomous driving","authors":"Yanjun Huang, Jiatong Du, Ziru Yang, Zewei Zhou, Lin Zhang, Hong Chen","year":"2022","venue":"IEEE Transactions on Intelligent Vehicles","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kim et al., 2017] ByeoungDo Kim, Chang Mook Kang, Jaekyum Kim, Seung Hi Lee, Chung Choo Chung, and Jun Won Choi. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In IEEE ITSC, pages 399-404. IEEE, 2017.","title":"Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network","authors":"Byeoungdo Kim, Chang Mook Kang, Jaekyum Kim, Seung Hi Lee, Chung Choo, Chung, Jun Won Choi","year":"2017","venue":"IEEE ITSC","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kim et al., 2020] Hayoung Kim, Dongchan Kim, Gihoon Kim, Jeongmin Cho, and Kunsoo Huh. Multi-head at- tention based probabilistic vehicle trajectory prediction. In 2020 IEEE Intelligent Vehicles Symposium (IV), pages 1720-1725. IEEE, 2020.","title":"Multi-head attention based probabilistic vehicle trajectory prediction","authors":"Kim","year":"2020","venue":"IEEE Intelligent Vehicles Symposium (IV)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Krajewski et al., 2018] Robert Krajewski, Julian Bock, Laurent Kloeker, and Lutz Eckstein. The highd dataset: A drone dataset of naturalistic vehicle trajectories on ger- man highways for validation of highly automated driving systems. In 21st ITSC, pages 2118-2125. IEEE, 2018.","title":"The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems","authors":"Robert Krajewski, Julian Bock, Laurent Kloeker, Lutz Eckstein","year":"2018","venue":"21st ITSC","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kuefler et al., 2017] Alex Kuefler, Jeremy Morton, Tim Wheeler, and Mykel Kochenderfer. Imitating driver be- havior with generative adversarial networks. In IEEE IV, pages 204-211. IEEE, 2017.","title":"Imitating driver behavior with generative adversarial networks","authors":"Alex Kuefler, Jeremy Morton, Tim Wheeler, Mykel Kochenderfer","year":"2017","venue":"IEEE IV","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lee et al., 2017] Namhoon Lee, Wongun Choi, Paul Ver- naza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker. Desire: Distant future prediction in dynamic scenes with interacting agents. In Proceedings of the IEEE CVPR, pages 336-345, 2017.","title":"Desire: Distant future prediction in dynamic scenes with interacting agents","authors":"Lee","year":"2017","venue":"Proceedings of the IEEE CVPR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lefkopoulos et al., 2020] Vasileios Lefkopoulos, Marcel Menner, Alexander Domahidi, and Melanie N Zeilinger. Interaction-aware motion prediction for autonomous driv- ing: A multiple model kalman filtering scheme. IEEE Robotics and Automation Letters, 6(1):80-87, 2020.","title":"Interaction-aware motion prediction for autonomous driving: A multiple model kalman filtering scheme","authors":"Vasileios Lefkopoulos, Marcel Menner, Alexander Domahidi, Melanie N Zeilinger","year":"2020","venue":"IEEE Robotics and Automation Letters","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liao et al., 2024a] Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang, Zhiyong Cui, Shengbo Eben Li, and Chengzhong Xu. A cognitive-based trajectory prediction approach for autonomous driving. IEEE Transactions on Intelligent Vehicles, pages 1-12, 2024.","title":"A cognitive-based trajectory prediction approach for autonomous driving","authors":"Liao","year":"2024","venue":"IEEE Transactions on Intelligent Vehicles","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liao et al., 2024b] Haicheng Liao, Zhenning Li, Huanming Shen, Wenxuan Zeng, Dongping Liao, Guofa Li, and Chengzhong Xu. Bat: Behavior-aware human-like trajec- tory prediction for autonomous driving. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 10332-10340, 2024.","title":"Bat: Behavior-aware human-like trajectory prediction for autonomous driving","authors":"Liao","year":"2024","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liao et al., 2024c] Haicheng Liao, Zhenning Li, Chengyue Wang, Huanming Shen, Bonan Wang, Dongping Liao, Guofa Li, and Chengzhong Xu. Mftraj: Map-free, behavior-driven trajectory prediction for autonomous driv- ing, 2024.","title":"Mftraj: Map-free, behavior-driven trajectory prediction for autonomous driving","authors":"Liao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2024/657"},{"position":23,"raw":"[Liao et al., 2024d] Haicheng Liao, Zhenning Li, Chengyue Wang, Bonan Wang, Hanlin Kong, Yanchen Guan, Guofa Li, Zhiyong Cui, and Chengzhong Xu. A cognitive- driven trajectory prediction model for autonomous driv- ing in mixed autonomy environment. arXiv preprint arXiv:2404.17520, 2024.","title":"A cognitivedriven trajectory prediction model for autonomous driving in mixed autonomy environment","authors":"Liao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liao et al., 2024e] Haicheng Liao, Shangqian Liu, Yongkang Li, Zhenning Li, Chengyue Wang, Bonan Wang, Yanchen Guan, and Chengzhong Xu. Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments. arXiv preprint arXiv:2402.04318, 2024.","title":"Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments","authors":"Liao","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liao et al., 2024f] Haicheng Liao, Huanming Shen, Zhen- ning Li, Chengyue Wang, Guofa Li, Yiming Bie, and Chengzhong Xu. Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models. Communications in Trans- portation Research, 4:100116, 2024.","title":"Gpt-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models","authors":"Liao","year":"2024","venue":"Communications in Transportation Research","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Messaoud et al., 2019] Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, and Fawzi Nashashibi. Non-local social pooling for vehicle trajectory prediction. In IEEE IV, pages 975-980. IEEE, 2019. [Messaoud et al., 2021] Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet, and Fawzi Nashashibi. Attention based vehicle trajectory prediction. IEEE Transactions on Intelligent Vehicles, 6(1):175-185, 2021.","title":"Non-local social pooling for vehicle trajectory prediction","authors":"Messaoud","year":"2019","venue":"IEEE IV","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Pan, 2020] Yunhe Pan. Multiple knowledge representa- tion of artificial intelligence. Engineering, 6(3):216-217, 2020.","title":"Multiple knowledge representation of artificial intelligence","authors":"Yunhe Pan, Pan","year":"2020","venue":"Engineering","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Poole et al., 2022] Ben Poole, Ajay Jain, Jonathan T Bar- ron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022.","title":"Dreamfusion: Text-to-3d using 2d diffusion","authors":"Ben Poole, Ajay Jain, Jonathan T Barron, Ben Mildenhall","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Prevost et al., 2007] Carole G Prevost, Andre Desbiens, and Eric Gagnon. Extended kalman filter for state estimation and trajectory prediction of a moving object detected by an unmanned aerial vehicle. In 2007 American control conference, pages 1805-1810. IEEE, 2007.","title":"Extended kalman filter for state estimation and trajectory prediction of a moving object detected by an unmanned aerial vehicle","authors":"Carole G Prevost, Andre Desbiens, Eric Gagnon","year":"2007","venue":"American control conference","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ramesh et al., 2022] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.","title":"Hierarchical text-conditional image generation with clip latents","authors":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF CVPR, pages 10684-10695, 2022.","title":"High-resolution image synthesis with latent diffusion models","authors":"Rombach","year":"2022","venue":"Proceedings of the IEEE/CVF CVPR","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tang and Salakhutdinov, 2019] Charlie Tang and Russ R Salakhutdinov. Multiple futures prediction. Advances in neural information processing systems, 32, 2019.","title":"Multiple futures prediction","authors":"Charlie Tang, Russ R Salakhutdinov","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2022] Yu Wang, Shengjie Zhao, Rongqing Zhang, Xiang Cheng, and Liuqing Yang. Multi-vehicle collaborative learning for trajectory prediction with spatio- temporal tensor fusion. IEEE Transactions on Intelligent Transportation Systems, 23(1):236-248, 2022.","title":"Multi-vehicle collaborative learning for trajectory prediction with spatiotemporal tensor fusion","authors":"Yu Wang, Shengjie Zhao, Rongqing Zhang, Xiang Cheng, Liuqing Yang","year":"2022","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2023] Renzhi Wang, Senzhang Wang, Hao Yan, and Xiang Wang. Wsip: Wave superposition inspired pooling for dynamic interactions-aware trajectory predic- tion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4685-4692, 2023.","title":"Wsip: Wave superposition inspired pooling for dynamic interactions-aware trajectory prediction","authors":"Wang","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xie et al., 2021] Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, and Song-Chun Zhu. Congestion-aware multi-agent trajectory prediction for collision avoidance. In 2021 IEEE International Conference on Robotics and Automa- tion (ICRA), pages 13693-13700. IEEE, 2021.","title":"Congestion-aware multi-agent trajectory prediction for collision avoidance","authors":"Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu","year":"2021","venue":"IEEE International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhao et al., 2019] Tianyang Zhao, Yifei Xu, Mathew Mon- fort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, and Ying Nian Wu. Multi-agent tensor fusion for contextual trajectory prediction. In Proceedings of the IEEE/CVF CVPR, pages 12126-12134, 2019.","title":"Multi-agent tensor fusion for contextual trajectory prediction","authors":"Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu","year":"2019","venue":"Proceedings of the IEEE/CVF CVPR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhou et al., 2021] Hao Zhou, Dongchun Ren, Huaxia Xia, Mingyu Fan, Xu Yang, and Hai Huang. Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction. Neuro- computing, 445:298-308, 2021.","title":"Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction","authors":"Hao Zhou, Dongchun Ren, Huaxia Xia, Mingyu Fan, Xu Yang, Hai Huang","year":"2021","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zuo et al., 2023] Zhiqiang Zuo, Xinyu Wang, Songlin Guo, Zhengxuan Liu, Zheng Li, and Yijing Wang. Trajectory prediction network of autonomous vehicles with fusion of historical interactive features. IEEE Transactions on Intel- ligent Vehicles, 2023.","title":"Trajectory prediction network of autonomous vehicles with fusion of historical interactive features","authors":"Zhiqiang Zuo, Xinyu Wang, Songlin Guo, Zhengxuan Liu, Zheng Li, Yijing Wang","year":"2023","venue":"IEEE Transactions on Intelligent Vehicles","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}