A Unified Environmental Network for Pedestrian Trajectory Prediction
Authors: Yuchao Su, Yuanman Li, Wei Wang, Jiantao Zhou, Xia Li
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the performance of our proposed model on four trajectory prediction datasets, encompassing both short-term and long-term predictions. The experimental results demonstrate the superiority of our approach over existing methods. Experiments We compare our model with ten algorithms on four datasets. |
| Researcher Affiliation | Academia | 1Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University 2Department of Engineering, Shenzhen MSU-BIT University 3Department of Computer and Information Science, University of Macau |
| Pseudocode | No | The paper describes its methodology through textual descriptions and diagrams but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about open-sourcing the code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We evaluate our proposed model on four pedestrian trajectory prediction datasets, i.e., ETH (Pellegrini et al. 2009), UCY (Lerner, Chrysanthou, and Lischinski 2007), Intersection Drone Dataset (in D) (Bock et al. 2020), and Stanford Drone Dataset (SDD) (Robicquet et al. 2016). |
| Dataset Splits | Yes | The process of all data follows the implementation in (Mangalam et al. 2021). |
| Hardware Specification | Yes | The proposed model runs in the Py Torch framework with an NVIDIA RTX 3090. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | z is set to 32. r and σ are set to 0.5 and 4 for the short-term prediction, and 2 and 16 for the long-term prediction. The training epoch is set to 500 with a batch size of 4, and Adam optimizer with a learning rate of 1e 4 is used. |