Enhancing Trajectory Prediction through Self-Supervised Waypoint Distortion Prediction
Authors: Pranav Singh Chib, Pravendra Singh
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |