Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Physics-Informed Trajectory Prediction for Autonomous Driving under Missing Observation
Authors: Haicheng Liao, Chengyue Wang, Zhenning Li, Yongkang Li, Bonan Wang, Guofa Li, Chengzhong Xu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations demonstrate that our approach markedly outperforms existing methods, achieving high accuracy even in scenarios with up to 75% missing observations. |
| Researcher Affiliation | Academia | 1University of Macau 2UESTC 3Chongqing University |
| Pseudocode | Yes | Algorithm 1 Risk Aware Algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement or direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We significantly enrich trajectory prediction research by introducing Mo CAD-missing, a dataset curated from a fully (Level 5) AV in real-world traffic scenarios, and by enhancing the established NGSIM and High D datasets with scenarios featuring missing observations. |
| Dataset Splits | Yes | These datasets provide longitudinal and lateral coordinates of traffic agents, which are systematically divided into training, validation, and test sets. |
| Hardware Specification | Yes | The model is trained on a single Nvidia A40 48GB GPU to convergence, using a learning rate of 0.0005 and a batch size of 64. |
| Software Dependencies | Yes | We use the Haar wavelet and its corresponding filters from Py Wavelet [Lee et al., 2019] |
| Experiment Setup | Yes | The model is trained on a single Nvidia A40 48GB GPU to convergence, using a learning rate of 0.0005 and a batch size of 64. We employ Mean Square Error (MSE) and Negative Log Likelihood (NLL) as loss functions. |