(ECCV 2020) PiP: Planning-informed Trajectory Prediction for Autonomous Driving
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.
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
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
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}
}