Awesome

This repo contains the official implementation of our paper: "End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps". Ke Guo, Wenxi Liu, Jia Pan.

CVPR 2022
paper

Installation

Environment

Python >= 3.7
PyTorch == 1.8.0

Data and pretrained model

Please download the pretrained model and data from onedrive(https://connecthkuhk-my.sharepoint.com/:u:/g/personal/u3006612_connect_hku_hk/EXqC6hjGTphKh8TkjrwtByEB3FFZ_dpCu0Rs6N7CTG2gag?e=5q4Knz). Extract the zip file into the main folder.

Data Preprocessing

Here is the detail of data preprocessing. You can skip it by using the data from google drive.

SDD (Trajnet split)

Download the Trajnet split data from Y-Net. Put the data under data/SDD
Run script to process the downloaded "train_trajnet.pkl" and "test_trajnet.pkl":
```
python data/SDD/process_trajnet.py
```

SDD(P2T split)

Download the P2T split data from P2T. Put the data under data/SDD
Run script to process the downloaded "SDDtrain.mat", "SDDval.mat" and "SDDtest.mat":
```
python data/SDD/process_p2t.py
```

Obtain the processed inD data from Y-Net. Put the data under data/SDD
Run script to process the downloaded "inD_train.pickle" and "inD_test.pickle":
```
python data/SDD/process_inD.py
```

Training

Training the model for Trajnet:

  ```
  python train.py  --dataset "trajnet"
  ```

For SDD(p2t split) or inD, the "trajnet" need to be replaced by "sdd" or "ind".

Evaluation

Evaluating on Trajnet dataset:

  ```
  python eval.py  --dataset "trajnet"
  ```

For SDD(p2t split) or inD, the "trajnet" need to be replaced by "sdd" or "ind".

Citation

@inproceedings{guo2022end,
  title={End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps},
  author={Ke, Guo and Wenxi, Liu and Jia, Pan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2022}
}