Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
Authors: Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D'Souza, Samira Ebrahimi Kahou, Felix Heide, Christopher Pal
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method with strong empirical results on diverse tasks, including the large-scale nu Scenes and Argoverse datasets for autonomous driving (Caesar et al., 2020; Chang et al., 2019), the multi-lingual Omniglot dataset of handwritten characters (Lake et al., 2015), and the synthetic partition of the Traj Net++ pedestrian trajectory prediction task (Sadeghian et al., 2018). |
| Researcher Affiliation | Collaboration | 1Polytechnique Montréal, 2Mila, Quebec AI Institute 3Element AI / Service Now, 4Independent Robotics, 5Algolux, 6Mc Gill University, 7École de technologie supérieure, 8Canada CIFAR AI Chair, 9Princeton University. |
| Pseudocode | No | The paper does not contain any block explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper mentions 'We implemented our model using the Pytorch open-source framework.' but this refers to a third-party framework, not the authors' own code for the described methodology. While a URL to a project page is provided, it does not explicitly state that the source code for the paper's methodology is available there. |
| Open Datasets | Yes | We evaluate Auto Bot on nu Scenes (Caesar et al., 2020) and Argoverse (Chang et al., 2019), two autonomous driving trajectory prediction benchmarks, on the synthetic partition of the Traj Net++ (Kothari et al., 2021) dataset, a pedestrian trajectory forecasting benchmark, and Omniglot (Lake et al., 2015), a dataset of hand-drawn characters that we use for predicting strokes. |
| Dataset Splits | Yes | There are a total of 54, 513 unique scenes in the dataset, which we split into 49, 062 training scenes and 5, 451 validation scenes. |
| Hardware Specification | Yes | A distinguishing feature of Auto Bots is that all models are trainable on a single desktop GPU (1080 Ti) in under 48h. ... Auto Bot-Ego was trained on a single Nvidia Geforce GTX 1080Ti GPU, using approximately 2 GB of VRAM. |
| Software Dependencies | No | The paper states 'We implemented our model using the Pytorch open-source framework,' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Table 4 shows the values of parameters used in our experiments across the different datasets. ... For the Nuscenes and Traj Net++ datasets, we anneal the learning rate every 10 epochs for the first 20 epochs by a factor of 2, followed by annealing it by a factor of 1.33 every 10 epochs for the next 30 epochs. ... we found it helpful to clip the gradients to a maximum magnitude of 5.0. |