Multiple Futures Prediction
Authors: Charlie Tang, Russ R. Salakhutdinov
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets. |
| Researcher Affiliation | Industry | Yichuan Charlie Tang Apple Inc. yichuan_tang@apple.com Ruslan Salakhutdinov Apple Inc. rsalakhutdinov@apple.com |
| Pseudocode | Yes | We provide a detailed training algorithm pseudocode in the supplementary materials. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the public release of its source code for the described methodology. |
| Open Datasets | Yes | We demonstrate our algorithms by predicting vehicle trajectories of both simulated and real data, demonstrating the state-of-the-art results on several vehicle trajectory datasets. ... First, we first generate simulated trajectory data from the CARLA simulator [17]... We then experiment on a widely known standard dataset of real vehicle trajectories, the NGSIM [12] dataset. ... Finally, we also benchmark MFP with previously published results on the more recent large scale Argoverse motion forecasting dataset [9]. |
| Dataset Splits | Yes | We experiment with the US-101 and I-80 datasets, and follow the experimental protocol of [16], where the datasets are split into 70% training, 10% validation, and 20% testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using GRUs and the CARLA simulator, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We extract 8 seconds trajectories, using the first 3 seconds as history to predict 5 seconds into the future. ... We trained MFP (with 1 to 5 modes) on the Town01 training set for 200K updates, with minibatch size 8. |