Codes-for-Steering-Control
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Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks (AAAI 2019, oral)
Codes for "Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks".
Besides, our project page is now available at FM-Net.
Demo video
- Performance of auxiliary networks on unseen target data:
- Performance of FM-Net:
Content:
- Installation
- Datasets
- Udacity
- Comma-ai
- BDD100K
- Semantic-Segmentation
- Steering-Control
- Test
- Train
- Performance
- Others
- Citation
- Acknowledgement
- Contact
Installations
conda create -n tensorflow_gpu pip python=2.7
source activate tensorflow_gpu
pip install --upgrade tensorflow-gpu==1.4
conda install pytorch torchvision -c pytorch
Datasets
Udacity
The whole dataset is available at Udacity.
Comma-ai
The whole dataset is available at Comma-ai.
BDD100K
The whole dataset is available at BDD100K.
Semantic-Segmentation
FCN (mIoU 71.03%)
cd semantic-segmentation
python3 main.py VOCAug FCN train val --lr 0.01 --gpus 0 1 2 3 4 5 6 7 --npb
PSPNet
python3 train_pspnet.py VOCAug PSPNet train val --lr 0.01 --gpus 0 1 2 3 4 5 6 7 --npb --test_size 473
Note that you can use the code to train models (e.g., PSPNet, SegNet and FCN) in Cityscape.
Steering-Control
Test
cd steering-control
CUDA_VISIBLE_DEVICES="0" python 3d_resnet_lstm.py
Note that you need to read 3d_resnet_lstm.py and options.py carefully and modify the path accordingly. Note that current setting is used for Udacity dataset. To run the codes for Comma.ai dataset, please refer to Comma-ai and our paper to modify several parameters.
Train
CUDA_VISIBLE_DEVICES="0" python 3d_resnet_lstm.py --flag train
Note that the ImageNet pre-trained model is available here.
Performance
- Udacity testing set:
| Model | MAE | RMSE |
|---|---|---|
| 3D CNN | 2.5598 | 3.6646 |
| 3D CNN + LSTM | 1.8612 | 2.7167 |
| 3D ResNet (ours) | 1.9167 | 2.8532 |
| 3D ResNet + LSTM (ours) | 1.7147 | 2.4899 |
| FM-Net (ours) | 1.6236 | 2.3549 |
- Comma-ai testing set:
| Model | MAE | RMSE |
|---|---|---|
| 3D CNN | 1.7539 | 2.7316 |
| 3D CNN + LSTM | 1.4716 | 1.8397 |
| 3D ResNet (ours) | 1.5427 | 2.4288 |
| 3D ResNet + LSTM (ours) | 0.7989 | 1.1519 |
| FM-Net (ours) | 0.7048 | 0.9831 |
- BDD100K testing set:
| Model | Accuracy |
|---|---|
| FCN + LSTM | 82.03% |
| 3D CNN + LSTM | 82.94% |
| 3D ResNet + LSTM (ours) | 83.69% |
| FM-Net (ours) | 85.03% |
Others
Citation
If you use the codes, please cite the following publications:
@article{hou2018learning,
title={Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks},
author={Hou, Yuenan and Ma, Zheng and Liu, Chunxiao and Loy, Chen Change},
journal={arXiv preprint arXiv:1811.02759},
year={2018}
}
Acknowledgement
This repo is built upon Udacity.
Contact
If you have any problems in reproducing the results, just raise an issue in this repo.
To-Do List:
-
[x] Release codes for steering control
-
[x] Attach original experimental results
-
[x] Clean all codes, make them readable and reproducable
-
[ ] Release codes for BDD100K dataset