VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
conda env create -f environment.yaml
This command will create a conda environment named vtgnet
conda activate vtgnet
python test.py
The result will be saved in folder test_results/ as the following image:
Download our dataset here and extract it into folder VTG-Driving-Dataset/
Alternate download link with code: 4xn8
VTG-Driving-Dataset
├─ clear_day/
├─ clear_sunset/
├─ foggy_day/
├─ rainy_day/
├─ rainy_sunset/
├─ dataset_left.csv
├─ dataset_right.csv
├─ dataset_straight.csv
This dataset is collected in CARLA simulator. The driving information (location, speed, control actions, etc.) is recorded in efo_info.csv for each episode. The setups are listed as following:
- Dynamic traffic with pedestrians and other vehicles
- Collected in Town01 at desired speed of 40 km/h
- Five weather conditions
- 100 driving episodes for each weather
- 16.6 hours, 288.7 km
- With behavoirs recovering from periodic off-center and off-orientation mistakes.
The dataset_{left, right, straight}.csv files keeps the training information extracted from the dataset.
python train.py --direction 2 --load_weights True --batch_size 15
--direction {0,1,2} 0: keep straight; 1: turn right; 2: turn left
--load_weights {True,False} load pre-trained weights on Robotcar or not
@article{Cai2020VTGNetAV,
title={VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments},
author={Peide Cai and Yuxiang Sun and H. Wang and M. Liu},
journal={IEEE Transactions on Intelligent Vehicles},
year={2020},
doi={10.1109/TIV.2020.3033878}
}