Planning-informed Trajectory Prediction (PiP)

The official implementation of "PiP: Planning-informed Trajectory Prediction for Autonomous Driving" (ECCV 2020),

by Haoran Song, Wenchao Ding, [Yuxuan Chen](), Shaojie Shen, Michael Yu Wang and Qifeng Chen.

Inform the multi-agent future prediction with ego vehicle's planning in a novel planning-prediction-coupled pipeline.

For more details, please refer to our project website / paper / arxiv.

Dependencies

conda create -n PIPrediction python=3.7
source activate PIPrediction

conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
conda install tensorboard=1.14.0
conda install numpy=1.16 scipy=1.4 h5py=2.10 future

Download

• Raw datasets: download NGSIM and highD, then process them into the required format (.mat) using the preprocessing code.
• Processed datasets: download from this link and save them in datasets/.
• Trained models: download from this link and save them in trained_models/.

Running

Training by sh scripts/train.sh or running

python train.py --name ngsim_demo --batch_size 64 --pretrain_epochs 5 --train_epochs 10 \
    --train_set ./datasets/NGSIM/train.mat \
    --val_set ./datasets/NGSIM/val.mat

Test by sh scripts/test.sh or running

python evaluate.py --name ngsim_model --batch_size 64 \
    --test_set ./datasets/NGSIM/test.mat

Documentation

• model.py : It contains the concrete details of the proposed PiP architecture.
• train.py : It contains the detailed approach for training PiP model. All the network parameters are provided by the default values.
• evaluate.py : It contains the approach for evaluating a trained model. The prediction precision is reported by RMSE & NLL values at future time frames.
• data.py : It contains the customized dataset class for handling and batching trajectory data
• utils.py : It contains the loss calculation functions and some other helper functions.
• preprocess/ : It contains Matlab code for preprocessing the raw data from NGSIM or HighD into the required format.

Citation

If you find our work useful in your research, please citing:

@InProceedings{song2020pip,
author = {Song, Haoran and Ding, Wenchao and Chen, Yuxuan and Shen, Shaojie and Wang, Michael Yu and Chen, Qifeng},
title = {PiP: Planning-informed Trajectory Prediction for Autonomous Driving},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {August},
year = {2020}
}