{"type":"ScholarlyArticle","title":"CVTP3D: Cross-view Trajectory Prediction Using Shared 3D Queries for Autonomous Driving","authors":[{"name":"Zijian Song","slug":"zijian-song","given":"Zijian","family":"Song","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Huikun Bi","slug":"huikun-bi","given":"Huikun","family":"Bi","affiliation":""},{"name":"Ruisi Zhang","slug":"ruisi-zhang","given":"Ruisi","family":"Zhang","affiliation":"Intuitive Surgical"},{"name":"Tianlu Mao","slug":"tianlu-mao","given":"Tianlu","family":"Mao","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Zhaoqi Wang","slug":"zhaoqi-wang","given":"Zhaoqi","family":"Wang","affiliation":"Beijing Key Laboratory of Mobile Computing and Pervasive Device , Institute of Computing Technology , Chinese Academy of Sciences"}],"abstract":"Trajectory prediction with uncertainty is a critical and challenging task for autonomous driving. Nowadays, we can easily access sensor data represented in multiple views. However, cross-view consistency has not been evaluated by the existing models, which might lead to divergences between the multimodal predictions from different views. It is not practical and effective when the network does not comprehend the 3D scene, which could cause the downstream module in a dilemma. Instead, we predicts multimodal trajectories while maintaining cross-view consistency. We presented a cross-view trajectory prediction method using shared 3D Queries (XVTP3D). We employ a set of 3D queries shared across views to generate multi-goals that are cross-view consistent. We also proposed a random mask method and coarse-to-fine cross-attention to capture robust cross-view features. As far as we know, this is the first work that introduces the outstanding top-down paradigm in BEV detection field to a trajectory prediction problem. The results of experiments on two publicly available datasets show that XVTP3D achieved state-of-the-art performance with consistent cross-view predictions.","doi":"10.24963/ijcai.2023/34","url":"https://ijcai.org/proceedings/2023/34","pdf":"https://ijcai.org/proceedings/2023/0034.pdf","markdown":"https://ijcai.org/proceedings/2023/34.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/34","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":34,"pages":{"first":298,"last":308},"keywords":["Computer Vision: CV: Machine learning for vision","Agent-based and Multi-agent Systems: MAS: Multi-agent learning","Agent-based and Multi-agent Systems: MAS: Human-agent interaction"],"videos":[],"references":[{"position":1,"raw":"[Bi et al., 2019] Huikun Bi, Zhong Fang, Tianlu Mao, Zhaoqi Wang, and Zhigang Deng. Joint prediction for kinematic trajectories in vehicle-pedestrian-mixed scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10383-10392, 2019.","title":"Joint prediction for kinematic trajectories in vehicle-pedestrian-mixed scenes","authors":"Bi","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bi et al., 2020] Huikun Bi, Ruisi Zhang, Tianlu Mao, Zhi- gang Deng, and Zhaoqi Wang. How can i see my future? fvtraj: Using first-person view for pedestrian trajectory prediction. In European Conference on Computer Vision, pages 576-593. Springer, 2020.","title":"How can i see my future? fvtraj: Using first-person view for pedestrian trajectory prediction","authors":"Huikun Bi, Ruisi Zhang, Tianlu Mao, Zhigang Deng, Zhaoqi Wang","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Caesar et al., 2020] Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621-11631, 2020.","title":"nuscenes: A multimodal dataset for autonomous driving","authors":"Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Casas et al., 2018] Sergio Casas, Wenjie Luo, and Raquel Urtasun. Intentnet: Learning to predict intention from raw sensor data. In Conference on Robot Learning, pages 947- 956. PMLR, 2018.","title":"Intentnet: Learning to predict intention from raw sensor data","authors":"Sergio Casas, Wenjie Luo, Raquel Urtasun","year":"2018","venue":"Conference on Robot Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chai et al., 2019] Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. arXiv preprint arXiv:1910.05449, 2019.","title":"Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction","authors":"Chai","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chang et al., 2019] Ming-Fang Chang, John Lambert, Pat- sorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ra- manan, et al. Argoverse: 3d tracking and forecasting with rich maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8748- 8757, 2019.","title":"Argoverse: 3d tracking and forecasting with rich maps","authors":"Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2021] Guangyi Chen, Junlong Li, Nuoxing Zhou, Liangliang Ren, and Jiwen Lu. Personalized trajec- tory prediction via distribution discrimination. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 15580-15589, 2021.","title":"Personalized trajectory prediction via distribution discrimination","authors":"Guangyi Chen, Junlong Li, Nuoxing Zhou, Liangliang Ren, Jiwen Lu","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2022a] Xuanyao Chen, Tianyuan Zhang, Yue Wang, Yilun Wang, and Hang Zhao. Futr3d: A unified sensor fusion framework for 3d detection. arXiv preprint arXiv:2203.10642, 2022.","title":"Futr3d: A unified sensor fusion framework for 3d detection","authors":"Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2022b] Yuxiao Chen, Boris Ivanovic, and Marco Pavone. Scept: Scene-consistent, policy-based tra- jectory predictions for planning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17103-17112, 2022.","title":"Scept: Scene-consistent, policy-based trajectory predictions for planning","authors":"Chen","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Choi et al., 2021] Chiho Choi, Joon Hee Choi, Jiachen Li, and Srikanth Malla. Shared cross-modal trajectory pre- diction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 244-253, 2021.","title":"Shared cross-modal trajectory prediction for autonomous driving","authors":"Chiho Choi, Joon, Hee Choi, Jiachen Li, Srikanth Malla","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dendorfer et al., 2021] Patrick Dendorfer, Sven Elflein, and Laura Leal-Taixé. Mg-gan: A multi-generator model pre- venting out-of-distribution samples in pedestrian trajec- tory prediction. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 13158- 13167, 2021.","title":"Mg-gan: A multi-generator model preventing out-of-distribution samples in pedestrian trajectory prediction","authors":"Patrick Dendorfer, Sven Elflein, Laura Leal-Taixé","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gao et al., 2020] Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid. Vectornet: Encoding hd maps and agent dynam- ics from vectorized representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11525-11533, 2020.","title":"Vectornet: Encoding hd maps and agent dynamics from vectorized representation","authors":"Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, Cordelia Schmid","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gilles et al., 2021] Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, and Fabien Moutarde. Home: Heatmap output for future motion estimation. In 2021 IEEE International Intelligent Trans- portation Systems Conference (ITSC), pages 500-507. IEEE, 2021.","title":"Home: Heatmap output for future motion estimation","authors":"Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde","year":"2021","venue":"IEEE International Intelligent Transportation Systems Conference (ITSC)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gilles et al., 2022] Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, and Fabien Moutarde. Gohome: Graph-oriented heatmap output for future motion estimation. In 2022 International Conference on Robotics and Automation (ICRA), pages 9107-9114. IEEE, 2022.","title":"Gohome: Graph-oriented heatmap output for future motion estimation","authors":"Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde","year":"2022","venue":"2022 International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Girase et al., 2021] Harshayu Girase, Haiming Gang, Srikanth Malla, Jiachen Li, Akira Kanehara, Karttikeya Mangalam, and Chiho Choi. Loki: Long term and key intentions for trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9803-9812, 2021.","title":"Loki: Long term and key intentions for trajectory prediction","authors":"Girase","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Gu et al., 2021] Junru Gu, Chen Sun, and Hang Zhao. Densetnt: End-to-end trajectory prediction from dense goal sets. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15303-15312, 2021.","title":"Densetnt: End-to-end trajectory prediction from dense goal sets","authors":"Gu","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Gu et al., 2022] Tianpei Gu, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie Zhou, and Jiwen Lu. Stochastic trajectory prediction via motion indeterminacy diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17113- 17122, 2022.","title":"Stochastic trajectory prediction via motion indeterminacy diffusion","authors":"Gu","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Gupta et al., 2018] Agrim Gupta, Justin Johnson, Li Fei- Fei, Silvio Savarese, and Alexandre Alahi. Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on com- puter vision and pattern recognition, pages 2255-2264, 2018.","title":"Social gan: Socially acceptable trajectories with generative adversarial networks","authors":"Gupta","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Huang et al., 2019] Yingfan Huang, Huikun Bi, Zhaoxin Li, Tianlu Mao, and Zhaoqi Wang. Stgat: Modeling spatial- temporal interactions for human trajectory prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6272-6281, 2019.","title":"Stgat: Modeling spatialtemporal interactions for human trajectory prediction","authors":"Huang","year":"2019","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kim et al., 2021] ByeoungDo Kim, Seong Hyeon Park, Seokhwan Lee, Elbek Khoshimjonov, Dongsuk Kum, Jun- soo Kim, Jeong Soo Kim, and Jun Won Choi. Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14636-14645, 2021.","title":"Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents","authors":"Kim","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"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 conference on computer vision and pattern recognition, pages 336-345, 2017.","title":"Desire: Distant future prediction in dynamic scenes with interacting agents","authors":"Lee","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2022] Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, and Jifeng Dai. Bevformer: Learning bird's-eye-view representation from multi-camera images via spatiotemporal transform- ers. arXiv preprint arXiv:2203.17270, 2022.","title":"Bevformer: Learning bird's-eye-view representation from multi-camera images via spatiotemporal transformers","authors":"Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, Jifeng Dai","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liang et al., 2020] Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun. Learning lane graph representations for motion forecast- ing. In European Conference on Computer Vision, pages 541-556. Springer, 2020.","title":"Learning lane graph representations for motion forecasting","authors":"Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lin et al., 2021] Lei Lin, Weizi Li, Huikun Bi, and Lingqiao Qin. Vehicle trajectory prediction using lstms with spatial- temporal attention mechanisms. IEEE Intelligent Trans- portation Systems Magazine, 14(2):197-208, 2021.","title":"Vehicle trajectory prediction using lstms with spatial-temporal attention mechanisms","authors":"Lei Lin, Weizi Li, Huikun Bi, Lingqiao Qin","year":"2021","venue":"IEEE Intelligent Transportation Systems Magazine","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2022] Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. Petr: Position embedding transfor- mation for multi-view 3d object detection. arXiv preprint arXiv:2203.05625, 2022.","title":"Petr: Position embedding transformation for multi-view 3d object detection","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Malla et al., 2020] Srikanth Malla, Behzad Dariush, and Chiho Choi. Titan: Future forecast using action pri- ors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11186- 11196, 2020.","title":"Titan: Future forecast using action priors","authors":"Srikanth Malla, Behzad Dariush, Chiho Choi","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Messaoud et al., 2021] Kaouther Messaoud, Nachiket Deo, Mohan M Trivedi, and Fawzi Nashashibi. Trajectory pre- diction for autonomous driving based on multi-head atten- tion with joint agent-map representation. In 2021 IEEE In- telligent Vehicles Symposium (IV), pages 165-170. IEEE, 2021.","title":"Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation","authors":"Messaoud","year":"2021","venue":"IEEE Intelligent Vehicles Symposium","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Mohamed et al., 2020] Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social- stgcnn: A social spatio-temporal graph convolutional neu- ral network for human trajectory prediction. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14424-14432, 2020.","title":"Socialstgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction","authors":"Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, Christian Claudel","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Phan-Minh et al., 2020] Tung Phan-Minh, Elena Corina Grigore, Freddy A Boulton, Oscar Beijbom, and Eric M Wolff. Covernet: Multimodal behavior prediction using trajectory sets. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 14074-14083, 2020.","title":"Covernet: Multimodal behavior prediction using trajectory sets","authors":"Tung Phan-Minh, Elena Corina Grigore, Freddy A Boulton, Oscar Beijbom, Eric M Wolff","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Rasouli et al., 2017] Amir Rasouli, Iuliia Kotseruba, and John K Tsotsos. Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 206-213, 2017.","title":"Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior","authors":"Amir Rasouli, Iuliia Kotseruba, John K Tsotsos","year":"2017","venue":"Proceedings of the IEEE International Conference on Computer Vision Workshops","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rasouli et al., 2019] Amir Rasouli, Iuliia Kotseruba, Toni Kunic, and John K Tsotsos. Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6262-6271, 2019.","title":"Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction","authors":"Amir Rasouli, Iuliia Kotseruba, Toni Kunic, John K Tsotsos","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Salzmann et al., 2020] Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone. Trajectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data. In European Conference on Computer Vision, pages 683-700. Springer, 2020.","title":"Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data","authors":"Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, Marco Pavone","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Shao et al., 2020] Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, and Tarek Abdelzaher. Controlvae: Controllable variational autoencoder. In International Conference on Machine Learning, pages 8655-8664. PMLR, 2020.","title":"Controlvae: Controllable variational autoencoder","authors":"Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Shi et al., 2021] Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Mo Zhou, Zhenxing Niu, and Gang Hua. Sgcn: Sparse graph convolution network for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8994-9003, 2021.","title":"Sgcn: Sparse graph convolution network for pedestrian trajectory prediction","authors":"Shi","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Sun et al., 2022] Qiao Sun, Xin Huang, Junru Gu, Brian C Williams, and Hang Zhao. M2i: From factored marginal trajectory prediction to interactive prediction. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6543-6552, 2022.","title":"M2i: From factored marginal trajectory prediction to interactive prediction","authors":"Qiao Sun, Xin Huang, Junru Gu, Brian C Williams, Hang Zhao","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Varadarajan et al., 2022] Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Khaled S Refaat, Nigamaa Nayakanti, Andre Cornman, Kan Chen, Bertrand Douil- lard, Chi Pang Lam, Dragomir Anguelov, et al. Multi- path++: Efficient information fusion and trajectory aggre- gation for behavior prediction. In 2022 International Con- ference on Robotics and Automation (ICRA), pages 7814- 7821. IEEE, 2022.","title":"Multi-path++: Efficient information fusion and trajectory aggregation for behavior prediction","authors":"Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava, Nigamaa Khaled S Refaat, Andre Nayakanti, Kan Cornman, Bertrand Chen, Chi Pang Douillard, Dragomir Lam, Anguelov","year":"2022","venue":"2022 International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2022a] Jingke Wang, Tengju Ye, Ziqing Gu, and Junbo Chen. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134-17142, 2022.","title":"Ltp: Lane-based trajectory prediction for autonomous driving","authors":"Wang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2022b] Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In Conference on Robot Learning, pages 180-191.","title":"Detr3d: 3d object detection from multi-view images via 3d-to-2d queries","authors":"Wang","year":"2022","venue":"Conference on Robot Learning","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"PMLR, 2022.","title":null,"authors":"Pmlr","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yao et al., 2019] Yu Yao, Mingze Xu, Chiho Choi, David J Crandall, Ella M Atkins, and Behzad Dariush. Egocen- tric vision-based future vehicle localization for intelligent driving assistance systems. In 2019 International Con- ference on Robotics and Automation (ICRA), pages 9711- 9717. IEEE, 2019.","title":"Egocentric vision-based future vehicle localization for intelligent driving assistance systems","authors":"Yu Yao, Mingze Xu, Chiho Choi, David J Crandall, Ella M Atkins, Behzad Dariush","year":"2019","venue":"2019 International Conference on Robotics and Automation (ICRA)","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yin et al., 2021] Ziyi Yin, Ruijin Liu, Zhiliang Xiong, and Zejian Yuan. Multimodal transformer networks for pedes- trian trajectory prediction. In IJCAI, pages 1259-1265, 2021.","title":"Multimodal transformer networks for pedestrian trajectory prediction","authors":"Ruijin Ziyi Yin, Zhiliang Liu, Zejian Xiong, Yuan","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/174"},{"position":42,"raw":"[Yu et al., 2020] Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi. Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In European Conference on Computer Vision, pages 507-523. Springer, 2020.","title":"Spatio-temporal graph transformer networks for pedestrian trajectory prediction","authors":"Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, Shuai Yi","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhao et al., 2020] Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid, et al. Tnt: Target-driven trajectory prediction. arXiv preprint arXiv:2008.08294, 2020.","title":"Tnt: Target-driven trajectory prediction","authors":"Hang Zhao, Jiyang Gao, Tian Lan, Chen Sun, Benjamin Sapp, Balakrishnan Varadarajan, Yue Shen, Yi Shen, Yuning Chai, Cordelia Schmid","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhou et al., 2022] Zikang Zhou, Luyao Ye, Jianping Wang, Kui Wu, and Kejie Lu. Hivt: Hierarchical vector trans- former for multi-agent motion prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 8823-8833, 2022.","title":"Hivt: Hierarchical vector transformer for multi-agent motion prediction","authors":"Zhou","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}