TrafficPredict
Pytorch implementation for the paper: TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (AAAI), Oral, 2019
The repo has been forked initially from Anirudh Vemula's repository for his paper Social Attention: Modeling Attention in Human Crowds (ICRA 2018). If you find this code useful in your research then please also cite Anirudh Vemula's paper.
Comparison of results:
| Methods | Paper ADE | This repo ADE | Paper FDE | This repo FDE |
|---|---|---|---|---|
| pedestrian | 0.091 | 0.088 | 0.150 | 0.132 |
| bicycle | 0.083 | 0.075 | 0.139 | 0.115 |
| vehicle | 0.080 | 0.090 | 0.131 | 0.153 |
| total | 0.085 | 0.084 | 0.141 | 0.133 |
Requirements
- Python 3
- Seaborn (https://seaborn.pydata.org/)
- PyTorch (http://pytorch.org/)
- Numpy
- Matplotlib
- Scipy
How to Run
- First
cd srnn - To train the model run
python train.py(See the code to understand all the arguments that can be given to the command) - To test the model run
python sample.py --epoch=nwhere n is the epoch at which you want to load the saved model. (See the code to understand all the arguments that can be given to the command)