This repository contains code for the paper "Testing DNN-based Autonomous Driving Systems under Critical Environmental Conditions"
- OS: Ubuntu 16.04
- Packages: Tensorflow 1.2.0, Keras 1.2.2, and Pytorch 0.4.1.
Note: TACTIC relies on MUNIT to produce testing driving scenes. See more details about MUNIT in the repository https://github.com/NVlabs/MUNIT.
The major contents of this repository are:
- example_scenes/ contains the examples of generated driving scenes by our method
- models/ contains the implementation of subject DNN-based autonomous driving systems
- munit/ contains code for MUNIT model
- testing/ contains code for our experiments
In our experiments, we considered three popular DNN-based ADs, which have been widely used in previous work, namely Dave-orig, Dave-dropout, and Chauffuer.
For Dave-orig and Dave-dropout, the implementations of the models and the corresponding saved weights can be found in https://github.com/peikexin9/deepxplore/tree/master/Driving
For Chauffeur, the implementation of the model and the corresponding saved weight can be found in https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/chauffeur.
TACTIC relies on MUNIT to encode the environmental condition space and generate testing driving scenes. The details of MUNIT can be found in https://github.com/NVlabs/MUNIT. All save weights of MUNIT models used in our experiments can be downloaded from here
- Udacity Dataset: Dataset in the Udacity self-driving car challenge