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 | Conference PDF | Archive PDF | Plain Text | 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. |