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..
Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction
Authors: Pranav Singh Chib, Pravendra Singh
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results highlight a significant improvement with relative percentage differences of 22.7%/38.9%, 33.8%/36.4%, and 16.60%/23.20% in ADE/FDE for the NBA, Traj Net++, and ETH-UCY datasets, respectively, compared to the baseline methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India. |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate the performance of SSWDP on three trajectory datasets: NBA (Zhan et al., 2018), a synthetic partition of Traj Net++ (Kothari et al., 2021), and ETH-UCY (Lerner et al., 2007; Pellegrini et al., 2009). |
| Dataset Splits | No | The paper mentions 'validation data' in Table 6 and Section 4.6.3 but does not explicitly specify the train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | Yes | as we processed both views in parallel rather than sequentially on the NVIDIA RTX A5000 GPU with AMD EPYC 7543 CPU |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used (e.g., Python, PyTorch, CUDA, or specific ML frameworks) to reproduce the experiments. |
| Experiment Setup | Yes | The values for the noise factor (ω) and λ used in our experimentation during the training of the model are provided in Table 10. Section 4.6.3 provides insight into the choice of the noise factor. |