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..
Multi-Stream Representation Learning for Pedestrian Trajectory Prediction
Authors: Yuxuan Wu, Le Wang, Sanping Zhou, Jinghai Duan, Gang Hua, Wei Tang
AAAI 2023 | Venue PDF | 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. |