Code artifacts for the paper "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems", published in the IEEE Transactions on Software Engineering (TSE). A preprint is available here.
Real-world Environment
Virtual Environment (Unity)
Sim2Real and Real2Sim translations by CycleGAN
The same DNN model deployed on a real-world electric vehicle and in a virtual simulated world
Real-time XTE predictions in the real-world with visual odometry
Corruptions (left) and Adversarial Examples (right)
Python3, git 64 bit, miniconda 3.7 64 bit. To modify the simulator (optional): Unity 2019.3.0f1
Software setup: We adopted the PyCharm Professional 2020.3, a Python IDE by JetBrains, and Python 3.7.
Hardware setup: Training the DNN models (self-driving cars) and CycleGAN on our datasets is computationally expensive. Therefore, we recommend using a machine with a GPU. In our setting, we ran our experiments on a machine equipped with a AMD Ryzen 5 processor, 8 GB of memory, and an NVIDIA GPU GeForce RTX 2060 with 6 GB of dedicated memory. Our trained models are available here.
We used Donkey Car v. 3.1.5. Make sure you correctly install the donkey car software, the necessary simulator software and our simulator (macOS only).
* git clone https://github.com/autorope/donkeycar.git
* git checkout a91f88d
* conda env remove -n donkey
* conda env create -f install/envs/mac.yml
* conda activate donkey
* pip install -e .\[pc\]
Data collection for a XTE predictor must be collected manually (or our datasets can be used). Alternatively, data can be collected by:
- Launching the Simulator.
- Selecting a log directory by clicking the 'log dir' button
- Selecting a preferred resolution (default is 320x240)
- Launching the Sanddbox Track scene and drive the car with the 'Joystick/Keyboard w Rec' button
- Driving the car
This will generate a dataset of simulated images and respective XTEs (labels). The simulated images have then to be converted using a CycleGAN network trained to do sim2real translation.
Once the dataset of converted images and XTEs is collected, use the train_xte_predictor.py notebook to train the xte predictor.
Connection
Donkey Car needs a static IP so that we can connect onto the car
ssh jetsonnano@<your-IP>
Pwd: <password>
Joystick Pairing
ds4drv &
PS4 controller: press PS + share and hold; starts blinking and pairing If [error][bluetooth] Unable to connect to detected device: Failed to set operational mode: [Errno 104] Connection reset by peer Try again When LED is green, connection is ok
python manage.py drive —js // does not open web UI
python manage.py drive // does open web UI for settiong a maximum throttle value
X -> E-Stop (negative acceleration) Share -> change the mode [user, local, local_angle]
Enjoy!
press PS and hold for 10 s to turn it off
Training
python train.py --model <modelname>.h5 --tub <data> --type <modeltype> --aug
Testing (nominal conditions)
For autonomus driving:
python manage.py drive --model [models/<model-name.h5>]
Go to: http://10.21.13.35:8887/drive Select “Local Pilot (d)”
Testing (corrupted conditions)
python manage.py drive --model [models/<model-name.h5>] [--corruption=<corruption>] [--severity=<severity>] [--delay=<delay>]
Testing (adversarial conditions)
python manage.py drive --model [models/<model-name.h5>] [--useadversarial] [--advimage=<file>] [--severity=<severity>] [--delay=<delay>]
If you use our work in your research, or it helps it, or if you simply like it, please cite it in your publications. Here is an example BibTeX entry:
@article{2022-Stocco-TSE,
author = {Andrea Stocco and Brian Pulfer and Paolo Tonella},
title = {{Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems}},
journal = {IEEE Transactions on Software Engineering},
year = {2022},
url = {https://ieeexplore.ieee.org/document/9869302},
publisher = {IEEE}
}