kay1794/densetnt

DenseTNT

Paper | Webpage

• This is the official implementation of the paper: DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets (ICCV 2021).
• DenseTNT v1.0 was released in November 1st, 2021.

Quick Start

Requires:

• Python 3.6+
• pytorch 1.6+

1) Install packages

 pip install -r requirements.txt

2) Install Argoverse API

https://github.com/argoai/argoverse-api

3) Compile Cython

Compile a .pyx file into a C file using Cython:

⚠️Recompiling is needed every time the pyx files are changed.

cd src/
cython -a utils_cython.pyx && python setup.py build_ext --inplace

Performance

Results on Argoverse motion forecasting validation set:

	minADE	minFDE	Miss Rate
DenseTNT w/ 100ms optimization	0.80	1.27	7.0%
DenseTNT w/ 100ms optimization (minFDE)	0.73	1.05	9.8%
DenseTNT w/ goal set predictor (online)	0.82	1.37	7.0%

Models

Suppose the training data of Argoverse motion forecasting is at ./train/data/.

DenseTNT

Train

OUTPUT_DIR=models.densetnt.1; \
python src/run.py --argoverse --future_frame_num 30 \
--do_train --data_dir train/data/ --output_dir ${OUTPUT_DIR} \
--hidden_size 128 --train_batch_size 64 --sub_graph_batch_size 4096 --use_map \
--core_num 16 --use_centerline --other_params semantic_lane direction l1_loss \
goals_2D enhance_global_graph subdivide lazy_points new laneGCN point_sub_graph \
stage_one stage_one_dynamic=0.95 laneGCN-4 point_level point_level-4 \
point_level-4-3 complete_traj complete_traj-3 \

Evaluate

Add --do_eval --eval_params optimization MRminFDE cnt_sample=9 opti_time=0.1 to the end of the training command.

Citation

If you find our work useful for your research, please consider citing the paper:

@inproceedings{densetnt,
  title={Densetnt: End-to-end trajectory prediction from dense goal sets},
  author={Gu, Junru and Sun, Chen and Zhao, Hang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={15303--15312},
  year={2021}
}