This is the official repository of
Rethink Imitation-based Planner for Autonomous Driving, Jie Cheng,Yingbing chen,Xiaodong Mei,Bowen Yang,Bo Li and Ming Liu, arXiv 2023
- A good starting point for research on learning-based planner on the nuPlan dataset. This repo provides detailed instructions on data preprocess, training and benchmark.
- A simple pure learning-based baseline model planTF, that achieves decent performance without any rule-based strategies or post-optimization.
- Get Started
- Set A1D0 up Environment
- Feature cache
- Training
- Trained models
- Evaluation
- Results
- Acknowledgements
- Citation
- setup the nuPlan dataset following the offiical-doc
- setup conda environment
conda create -n plantf python=3.9
conda activate plantf
# install nuplan-devkit
git clone https://github.com/motional/nuplan-devkit.git && cd nuplan-devkit
pip install -e .
pip install -r ./requirements.txt
# setup planTF
cd ..
git clone https://github.com/jchengai/planTF.git && cd planTF
sh ./script/setup_env.sh
Preprocess the dataset to accelerate training. The following command generates 1M frames of training data from the whole nuPlan training set. You may need:
- change
cache.cache_pathto suit your condition - decrease/increase
worker.threads_per_nodedepends on your RAM and CPU.
export PYTHONPATH=$PYTHONPATH:$(pwd)
python run_training.py \
py_func=cache +training=train_planTF \
scenario_builder=nuplan \
cache.cache_path=/nuplan/exp/cache_plantf_1M \
cache.cleanup_cache=true \
scenario_filter=training_scenarios_1M \
worker.threads_per_node=40This process may take some time, be patient (20+hours in my setting).
We modified the training scipt provided by nuplan-devkit a little bit for more flexible training. By default, the training script will use all visible GPUs for training. PlanTF is quite lightweight, which takes about 4~6G GPU memory under the batch size of 32 (each GPU).
CUDA_VISIBLE_DEVICES=0,1,2,3 python run_training.py \
py_func=train +training=train_planTF \
worker=single_machine_thread_pool worker.max_workers=32 \
scenario_builder=nuplan cache.cache_path=/nuplan/exp/cache_plantf_1M cache.use_cache_without_dataset=true \
data_loader.params.batch_size=32 data_loader.params.num_workers=32 \
lr=1e-3 epochs=25 warmup_epochs=3 weight_decay=0.0001 \
lightning.trainer.params.val_check_interval=0.5 \
wandb.mode=online wandb.project=nuplan wandb.name=plantfyou can remove wandb related configurations if your prefer tensorboard.
Place the trained models at planTF/checkpoints/
| Model | Document | Download |
|---|---|---|
| PlanTF (state6+SDE) | - | OneDrive |
| RasterModel | Doc | OneDrive |
| UrbanDriver (openloop) | Doc | OneDrive |
- run a single scenario simulation (for sanity check):
sh ./script/plantf_single_scenarios.sh - run Test14-random:
sh ./script/plantf_benchmarks.sh test14-random - run Test14-hard:
sh ./script/plantf_benchmarks.sh test14-hard - run Val14 (this may take a long time):
sh ./script/plantf_benchmarks.sh val14
| Planners | Test14-random | Test14-hard | ||||||
|---|---|---|---|---|---|---|---|---|
| Type | Method | OLS↑ | NR-CLS↑ | R-CLS↑ | OLS↑ | NR-CLS↑ | R-CLS↑ | Time |
| Expert | LogReplay | 100.0 | 94.03 | 75.86 | 100.0 | 85.96 | 68.80 | - |
| Rule-based | IDM | 34.15 | 70.39 | 72.42 | 20.07 | 56.16 | 62.26 | 32 |
| PDM-Closed | 46.32 | 90.05 | 91.64 | 26.43 | 65.07 | 75.18 | 140 | |
| Hybrid | GameFormer | 79.35 | 80.80 | 79.31 | 75.27 | 66.59 | 68.83 | 443 |
| PDM-Hybrid | 82.21 | 90.20 | 91.56 | 73.81 | 65.95 | 75.79 | 152 | |
| Learning-based |
PlanCNN | 62.93 | 69.66 | 67.54 | 52.4 | 49.47 | 52.16 | 82 |
| UrbanDriver † | 82.44 | 63.27 | 61.02 | 76.9 | 51.54 | 49.07 | 124 | |
| GC-PGP | 77.33 | 55.99 | 51.39 | 73.78 | 43.22 | 39.63 | 160 | |
| PDM-Open | 84.14 | 52.80 | 57.23 | 79.06 | 33.51 | 35.83 | 101 | |
| PlanTF (Ours) | 87.07 | 86.48 | 80.59 | 83.32 | 72.68 | 61.7 | 155 | |
† open-loop re-implementation
| Method | OLS | NR-CLS | R-CLS |
|---|---|---|---|
| Log-replay | 100 | 94 | 80 |
| IDM | 38 | 77 | 76 |
| GC-PGP | 82 | 57 | 54 |
| PlanCNN | 64 | 73 | 72 |
| PDM-Hybrid | 84 | 93 | 92 |
| PlanTF (Ours) | 89.18 | 84.83 | 76.78 |
Many thanks to the open-source community, also checkout these works:
If you find this repo useful, please consider giving us a star 🌟 and citing our related paper.
@misc{cheng2023plantf,
title={Rethinking Imitation-based Planner for Autonomous Driving},
author={Jie Cheng and Yingbing Chen and Xiaodong Mei and Bowen Yang and Bo Li and Ming Liu},
year={2023},
eprint={2309.10443},
archivePrefix={arXiv},
primaryClass={cs.RO}
}