The official pytorch implementation of NLE-DM (Natural-Language Explanations for Decision Making).
Clone this repo and prepare the environment.
git clone https://github.com/lab-sun/NLE-DM.git
cd NLE_DM
conda env create -f environment.yml --name NLE_DM
conda activate NLE_DM
Download the dataset, create the foler Data
and release it into the Data
;
BDD10K for the pre-training and obtain the semantic segmentation of road scene
BDD_OIA for the Act-Rea sub-network (jointly predict actions and reasons)
BDD_AD for the Act-Desc sub-network (jointly predict actions and descriptions)
Download the pretrained weight, create the foler weight
and put into the weight
(optional);
To train the network, select the appropriate .py in the folder of train
pre_train.py: To pretrain the network.
train_act_exp.py: To train the Act-Rea sub-network
train_act_des.py: To train the Act-Desc sub-network
To obatin the prediction results, select the appropriate .py in the folder of predict
predict_act_rea.py: To jointly predict the driving actions and correpsonding reasons.
predict_act_desc.py: To jointly predict the driving actions and environment descriptions.
Download the datasets and then extract it in the folder of Data
weight
bdd10k_resnet50_1.pth: weight of pre-training on BDD10K
act_rea_resnet50.pth: weight of Act-Rea sub-network
act_des_resnet50.pth: weight of Act-Desc sub-network (backbone: ResNet 50)
If you use our NEL-DM network or BDD-AD dataset in an academic work, please cite:
@ARTICLE{10144484,
author={Feng, Yuchao and Hua, Wei and Sun, Yuxiang},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={NLE-DM: Natural-Language Explanations for Decision Making of Autonomous Driving Based on Semantic Scene Understanding},
year={2023},
volume={},
number={},
pages={1-12},
doi={10.1109/TITS.2023.3273547}}
If you have any questions, pleas feel free to contact us!
Contact: yx.sun@polyu.edu.hk
Website: https://yuxiangsun.github.io/