Multi-Stream Representation Learning for Pedestrian Trajectory Prediction
Authors: Yuxuan Wu, Le Wang, Sanping Zhou, Jinghai Duan, Gang Hua, Wei Tang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed method on two commonly used datasets, i.e., ETH-UCY and SDD, and the experimental results demonstrate that our method achieves state-of-the-art performance. Code: https://github.com/Yuxuan IAIR/MSRL-master |
| Researcher Affiliation | Collaboration | 1Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 2School of Software Engineering, Xi an Jiaotong University 3Wormpex AI Research 4University of Illinois at Chicago |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams (Figure 2, Figure 3) but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code: https://github.com/Yuxuan IAIR/MSRL-master |
| Open Datasets | Yes | We evaluate our model on the ETH-UCY (Pellegrini et al. 2009; Lerner, Chrysanthou, and Lischinski 2007) and Stanford Drone Dataset (SDD) (Bock et al. 2020), which are widely-used benchmarks for pedestrian trajectory prediction. |
| Dataset Splits | Yes | Following prior work, we use the leave-one-out cross validation strategy on ETH-UCY. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |