Trajectory Prediction using Equivariant Continuous Convolution
Authors: Robin Walters, Jinxi Li, Rose Yu
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on two real-world trajectory datasets: Argoverse autonomous vehicle dataset (Chang et al., 2019) and Traj Net++ pedestrian trajectory forecasting challenge (Kothari et al., 2020). |
| Researcher Affiliation | Academia | Robin Walters Northeastern University r.walters@northeastern.edu Jinxi Li Northeastern University li.jinxi1@northeastern.edu Rose Yu University of California, San Diego roseyu@ucsd.edu |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data can be found at https://github.com/Rose-STL-Lab/ECCO. |
| Open Datasets | Yes | We evaluate our model on two real-world trajectory datasets: Argoverse autonomous vehicle dataset (Chang et al., 2019) and Traj Net++ pedestrian trajectory forecasting challenge (Kothari et al., 2020). |
| Dataset Splits | Yes | For Argoverse, the task is to predict three-second trajectories based on all vehicles history in the past 2 seconds. We split 32K samples from the validation set as our test set. Argoverse dataset includes 324K samples, which are split into 206K training data, 39K validation and 78K test set. |
| Hardware Specification | No | The paper mentions running experiments 'on the same test machine' but does not specify any particular hardware components like CPU or GPU models, or memory details. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and implies the use of a programming language, but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Our models are all trained by Adam optimizer with base learning rate 0.001, and the gamma rate for linear rate scheduler is set to be 0.95. All our models without map information are trained for 15K iterations with batch size 16 and learning rate is updated every 300 iterations; for models with map information, we train them for 30K iterations with batch size 16 and learning rate is updated every 600 iterations. For Cts Conv, we set the layer sizes to be 32, 64, 64, 64, and kernel size 4 4 4; for ρ1-ECCO, the layer sizes are 16, 32, 32, 32, kθ is 16, kr is 3; for ρreg-ECCO, we choose layer size 8, 16, 8, 8, kθ 16, kr 3, and regular feature dimension is set to be 8. |