PlanScope

This is the official repository of

PlanScope: Learning to Plan Within Decision Scope Does Matter

Ren Xin, Jie Cheng, and Jun Ma

Setup Environment

Setup dataset

Setup the nuPlan dataset following the offiical-doc

Setup conda environment

conda create -n planscope python=3.9
conda activate planscope

# install nuplan-devkit
git clone https://github.com/motional/nuplan-devkit.git && cd nuplan-devkit
pip install -e .
pip install -r ./requirements.txt

# setup planscope
cd ..
git clone https://github.com/Rex-sys-hk/PlanScope && cd planscope
sh ./script/setup_env.sh

Feature Cache

Preprocess the dataset to accelerate training. It is recommended to run a small sanity check to make sure everything is correctly setup.

 python run_training.py \
    py_func=cache +training=train_pluto \
    scenario_builder=nuplan_mini \
    cache.cache_path=/nuplan/exp/sanity_check \
    cache.cleanup_cache=true \
    scenario_filter=training_scenarios_tiny \
    worker=sequential

Then preprocess the whole nuPlan training set (this will take some time). You may need to change cache.cache_path to suit your condition

 export PYTHONPATH=$PYTHONPATH:$(pwd)

 python run_training.py \
    py_func=cache +training=train_pluto \
    scenario_builder=nuplan \
    cache.cache_path=/nuplan/exp/cache_pluto_1M \
    cache.cleanup_cache=true \
    scenario_filter=training_scenarios_1M \
    worker.threads_per_node=40

Training

sh train_scope.sh

• you can remove wandb related configurations if your prefer tensorboard.

Checkpoint

Copy your the chekpoint path to sim_scope.sh or sim_pluto.sh and replace the value of CKPT_N to run the evaluation.

Model	Download
Pluto-aux-nocil-m6-baseline	OneDrive
PlanScope-h10-m6	OneDrive
PlanScope-h20-m6	OneDrive

Run PlanScope-planner simulation

Run simulation for a random scenario in the nuPlan-mini split

sh ./sim_scope.sh

To Do

The code is under cleaning and will be released gradually.

• improve docs
• training code
• visualization
• Scope-planner & checkpoint
• feature builder & model
• initial repo & paper

Citation

If you find this repo useful, please consider giving us a star 🌟 and citing our related paper.

@misc{planscope,
      title={{PlanScope:} Learning to Plan Within Decision Scope Does Matter}, 
      author={Ren Xin and Jie Cheng and Jun Ma},
      year={2024},
      eprint={2411.00476},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2411.00476}, 
}

Thanks

• tuplan
• pluto

Special Announcement

This work investigates a technique to enhance the performance of planning models in a pure learning framework. We have deliberately omitted the rule-based pre- and post-processing modules from the baseline approach to mitigate the impact of artificially crafted rules, as claimed in our paper. A certain unauthorized publication led to inaccuracies in the depiction of its state-of-the-art (SOTA) capabilities. We hereby clarify this to prevent misunderstanding.

Nevertheless, the method introduced in our article is worth trying and could potentially serve as an add-on to augment the performance of the models you are developing, especially when the dataset is small. We are open to sharing and discussing evaluation results to foster a collaborative exchange.