Residential College | false |
Status | 已發表Published |
Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation | |
Haicheng Liao1; Chengyue Wang1; Zhenning Li1; Yongkang Li2; Bonan Wang1; Guofa Li3; Chengzhong Xu1 | |
2024 | |
Conference Name | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
Source Publication | Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence |
Pages | 6841-6849 |
Conference Date | 3-9 August 2024 |
Conference Place | Jeju, South Korea |
Country | Republic of Korea |
Publisher | International Joint Conferences on Artificial Intelligence |
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. |
Keyword | Robotics Planning And Scheduling Planning Under Uncertainty |
DOI | 10.24963/ijcai.2024/756 |
Language | 英語English |
Scopus ID | 2-s2.0-85204296427 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | Faculty of Science and Technology THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Zhenning Li |
Affiliation | 1.University of Macau, Macao 2.UESTC, China 3.Chongqing University, China |
First Author Affilication | University of Macau |
Corresponding Author Affilication | University of Macau |
Recommended Citation GB/T 7714 | Haicheng Liao,Chengyue Wang,Zhenning Li,et al. Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation[C]:International Joint Conferences on Artificial Intelligence, 2024, 6841-6849. |
APA | Haicheng Liao., Chengyue Wang., Zhenning Li., Yongkang Li., Bonan Wang., Guofa Li., & Chengzhong Xu (2024). Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 6841-6849. |
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