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]
In this repository, you can expect to find the following features š¤©:
Not included šµ:
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
python preprocess.py \
--root_dir path/to/your/Waymo_Dataset/scenario/ \
--save_dir path/to/your/processed_data/ \
--processes=16
scenario/validation
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/
python testing.py \
--data_dir path/to/your/testing_data/ \
--model_dir path/to/pretrained/model/
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}
}