JSEP 2021 Replication Package

This repository contains the artifacts attached to the paper "Confidence-driven Weighted Retraining for Predicting Safety-Critical Failures in Autonomous Driving Systems" by Andrea Stocco (USI) and Paolo Tonella (USI).

Reference

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{2021-Stocco-JSEP, author = {Andrea Stocco and Paolo Tonella}, title = {Confidence-driven Weighted Retraining for Predicting Safety-Critical Failures in Autonomous Driving Systems}, journal = {Journal of Software: Evolution and Process}, pages = {}, publisher = {John Wiley & Sons}, url = {https://doi.org/10.1002/smr.2386}, doi = {10.1002/smr.2386}, year = {2021} }

Simulation platform

You need to clone this version of the Udacity simulator for self-driving cars.

Dependencies

Software setup: We adopted the PyCharm Professional 2020.3, a Python IDE by JetBrains.

First, you need anaconda or miniconda installed on your machine. Then, you can create and install all dependencies on a dedicated virtual environment, by running one of the following commands, depending on your platform.

```python

macOS

conda env create -f environments.yml

Windows

conda env create -f windows.yml ```

Alternatively, you can manually install the required libraries (see the contents of the *.yml files) using pip.

Hardware setup: Training the DNN models (self-driving cars and autoencoders) 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.

Training

Train the autoencoders

• Copy the config config.py.sample into a custom config file, e.g., config_my.py
• Place the training set under the datasets folder. Set up the parameters under the section project settings
• Configure the parameters under the section autoencoder-based self-assessment oracle settings
• Run the file selforacle/vae_train.py

Train the self-driving car models

• Copy the config config.py.sample into a custom config file, e.g., config_my.py
• Place the training set under the datasets folder. Set up the parameters under the section project settings
• Configure the parameters under the section self-driving car model settings
• Run the file self_driving_car_train.py

Usage

• Copy the autoencoder within the sao folder. Set up the parameters, including the self-driving car model to use, under the section Udacity simulation settings
• Start up the Udacity self-driving simulator, choose a scene, and press the Autonomous Mode button. Then, run the file self_driving_car_drive.py

Replicate JSEP 2021 experiments

Datasets

Datasets for the experiment have a combined size of 35 GB. They are available upon request. For specific settings, it is more convenient to train your own models using our scripts (you do not need the simulator):

• Run the file scripts/vae_train_all.py
• Run the file scripts/vae_evaluate_class_imbalance_all.py
• Run the file scripts/vae_evaluate_novelty_detection_all.py

Improved Udacity simulator

Video of the simulator

Note: Click on the screenshot image to watch a demo video.

License

MIT. See the LICENSE.md file.

Contacts

For any questions, feel free to contact Andrea Stocco (andrea.stocco@usi.ch).