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.
• Updates:
  • July 25th, 2022: Add detailed code comments.

Quick Start

Requires:

• Python ≥ 3.6
• PyTorch ≥ 1.6

1) Install Packages

 pip install -r requirements.txt

2) Install Argoverse API

The latest version of Argoverse requires Python ≥ 3.7

If using Python 3.6, you can install Argoverse v1.0

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

3) Compile Cython

Compile a .pyx file into a C file using Cython (already installed at step 1):

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

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

Performance

Results on Argoverse motion forecasting validation set:

	minADE	minFDE	Miss Rate
DenseTNT w/ 100ms optimization (Miss Rate)	0.80	1.27	7.0%
DenseTNT w/ 100ms optimization (minFDE)	0.73	1.05	9.8%
DenseTNT w/ goal set predictor (Miss Rate)	0.82	1.37	7.0%
DenseTNT w/ goal set predictor (minFDE)	0.75	1.05	9.7%

A miss rate between 6.9% and 7.1% is considered reasonable. A minFDE between 1.05 and 1.06 is considered reasonable.

DenseTNT

1) Train

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

OUTPUT_DIR=models.densetnt.1; \
GPU_NUM=8; \
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 --use_map \
  --core_num 16 --use_centerline --distributed_training ${GPU_NUM} \
  --other_params \
    semantic_lane direction l1_loss \
    goals_2D enhance_global_graph subdivide goal_scoring laneGCN point_sub_graph \
    lane_scoring complete_traj complete_traj-3 \

Training takes 20 minutes per epoch and 5 hours for the default 16 epochs on 8 × 2080Ti.

2) Evaluate

Suppose the validation data of Argoverse motion forecasting is at ./val/data/.

• Optimize Miss Rate:

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

• Optimize minFDE:

  • Add --do_eval --eval_params optimization MRminFDE=0.0 cnt_sample=9 opti_time=0.1 to the end of the training command.

3) Train Set Predictor (Optional)

Compared with the optimization algorithm (default setting), the set predictor has similar performance but faster inference speed.

After training DenseTNT, suppose the model path is at models.densetnt.1/model_save/model.16.bin. The command for training the set predictor is:

OUTPUT_DIR=models.densetnt.set_predict.1; \
MODEL_PATH=models.densetnt.1/model_save/model.16.bin; \
GPU_NUM=8; \
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 --use_map \
  --core_num 16 --use_centerline --distributed_training ${GPU_NUM} \
  --other_params \
    semantic_lane direction l1_loss \
    goals_2D enhance_global_graph subdivide goal_scoring laneGCN point_sub_graph \
    lane_scoring complete_traj \
    set_predict=6 set_predict-6 data_ratio_per_epoch=0.4 set_predict-topk=0 set_predict-one_encoder set_predict-MRratio=1.0 \
    set_predict-train_recover=${MODEL_PATH} \

This training command optimizes Miss Rate. To optimize minFDE, change set_predict-MRratio=1.0 in the command to set_predict-MRratio=0.0.

To evaluate the set predictor, just add --do_eval to the end of this 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}
}