{"type":"ScholarlyArticle","title":"Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation","authors":[{"name":"Haicheng Liao","slug":"haicheng-liao","given":"Haicheng","family":"Liao","affiliation":"University of Macau"},{"name":"Chengyue Wang","slug":"chengyue-wang","given":"Chengyue","family":"Wang","affiliation":"University of Macau"},{"name":"Zhenning Li","slug":"zhenning-li","given":"Zhenning","family":"Li","affiliation":"University of Macau"},{"name":"Yongkang Li","slug":"yongkang-li","given":"Yongkang","family":"Li","affiliation":"UESTC"},{"name":"Bonan Wang","slug":"bonan-wang","given":"Bonan","family":"Wang","affiliation":"University of Macau"},{"name":"Guofa Li","slug":"guofa-li","given":"Guofa","family":"Li","affiliation":"Chongqing University"},{"name":"Chengzhong Xu","slug":"chengzhong-xu","given":"Chengzhong","family":"Xu","affiliation":"University of Macau"}],"abstract":"This paper introduces a novel trajectory prediction approach for autonomous vehicles (AVs), adeptly addressing the challenges of missing observations and the need for adherence to physical laws in real-world driving environments. This study proposes a hierarchical two-stage trajectory prediction model for AVs. In the first stage we propose the Wavelet Reconstruction Network, an innovative tool expertly crafted for reconstructing missing observations, offering optional integration with state-of-the-art models to enhance their robustness. Additionally, the second stage of the model features the Wave Fusion Encoder, a quantum mechanics-inspired innovation for sophisticated vehicle interaction modeling. By incorporating the Kinematic Bicycle Model, we ensure that our predictions align with realistic vehicular kinematics. Complementing our methodological advancements, we introduce MoCAD-missing, a comprehensive real-world traffic dataset, alongside enhanced versions of the NGSIM and HighD datasets, designed to facilitate rigorous testing in environments with missing observations. Extensive evaluations demonstrate that our approach markedly outperforms existing methods, achieving high accuracy even in scenarios with up to 75% missing observations.","doi":"10.24963/ijcai.2024/756","url":"https://ijcai.org/proceedings/2024/756","pdf":"https://ijcai.org/proceedings/2024/0756.pdf","markdown":"https://ijcai.org/proceedings/2024/756.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/756","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":"Robotics","track":"Main Track","number":756,"pages":{"first":6841,"last":6849},"keywords":["Robotics: ROB: Other","Planning and Scheduling: PS: Planning under uncertainty"],"videos":[],"references":[{"position":1,"raw":"[Arndt et al., 1999] Markus Arndt, Olaf Nairz, Julian Vos- Andreae, Claudia Keller, Gerbrand Van der Zouw, and An- ton Zeilinger. 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