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OPGP

This repo is the implementation of:

Occupancy Prediction-Guided Neural Planner for Autonomous Driving
Haochen Liu, Zhiyu Huang, Chen Lv
AutoMan Research Lab, Nanyang Technological University
[Paper]  [arXiv]  [Zhihu] 

  • Code is now released 😀!

Overview

In this repository, you can expect to find the following features 🤩:

  • Pipelines for data process and training
  • Open-loop evaluations

Not included 😵:

  • Model weights (Due to license from WOMD)
  • Real-time planning (Codes are not optimized for real-time performance)

Experiment Pipelines

Dataset and Environment

  • Downloading Waymo Open Motion Dataset v1.1. Utilize data from scenario/training_20s for train set, and data from scenario/validation for val & test.

  • Clone this repository and install required packages.

  • [NOTED] For theseus library, you may build from scratch and add system PATH in planner.py

Data Process

  • Preprocess data for training & testing:
python preprocess.py \
--root_dir path/to/your/Waymo_Dataset/scenario/ \
--save_dir path/to/your/processed_data/ \
--processes=16
  • You may also refer to Waymo_candid_list for more interactive and safety-critical scenarios filtered in scenario/validation

Training & Testing

  • Train & Eval the model using the command:
python -m torch.distributed.launch \
        --nproc_per_node 1 \ # number of gpus
        --master_port 16666 \
        training.py \
        --data_dir path/to/your/processed_data/ \
        --save_dir path/to/save/your/logs/
  • Conduct Open-loop Testing using the command:
python testing.py \
        --data_dir path/to/your/testing_data/ \
        --model_dir path/to/pretrained/model/

Citation

If you find this repository useful for your research, please consider giving us a star 🌟 and citing our paper.

@inproceedings{liu2023occupancy,
  title={Occupancy prediction-guided neural planner for autonomous driving},
  author={Liu, Haochen and Huang, Zhiyu and Lv, Chen},
  booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
  pages={4859--4865},
  year={2023},
  organization={IEEE}
}