ASE 2022

Artifacts accompanying the paper "ThirdEye: Attention Maps for Safe Autonomous Driving Systems" published at 37th IEEE/ACM Automated Software Engineering Conference (ASE 2022). A preprint is available here.

Overview

Our tool ThirdEye leverages the attention maps by the explainable AI algorithms SmoothGrad and turn them into a confidence score to automatically predict incoming failures of a DNN-based autonomous driving system (ADS). The figure below shows the attention maps of a confident ADS.

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.

# 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.

Replicate ASE 2022 experiments

Datasets & Simulator

Driving datasets, self-driving car models, and simulator have a combined size of several GBs. We will share them on demand.

Mutants

We use the following mutation operators:

udacity_add_weights_regularisation_mutated0_MP_l1_3_1
udacity_add_weights_regularisation_mutated0_MP_l1_l2_3_2
udacity_add_weights_regularisation_mutated0_MP_l2_3_0
udacity_change_activation_function_mutated0_MP_exponential_4_0
udacity_change_activation_function_mutated0_MP_hard_sigmoid_4_0
udacity_change_activation_function_mutated0_MP_relu_4_2
udacity_change_activation_function_mutated0_MP_selu_4_0
udacity_change_activation_function_mutated0_MP_sigmoid_4_3
udacity_change_dropout_rate_mutated0_MP_0.125_0.125_6_2
udacity_change_dropout_rate_mutated0_MP_1.0_1.0_6_1
udacity_change_activation_function_mutated0_MP_softmax_4_4
udacity_change_activation_function_mutated0_MP_softsign_4_5
udacity_change_activation_function_mutated0_MP_tanh_4_2
udacity_change_dropout_rate_mutated0_MP_0.25_0.25_6_7
udacity_change_dropout_rate_mutated0_MP_0.75_0.75_6_0
udacity_change_label_mutated0_MP_12.5_4
udacity_change_label_mutated0_MP_25.0_1
udacity_change_loss_function_mutated0_MP_mean_absolute_error_2

Evaluation Scripts

For replicating the RQs, you can run:

For ThidEye HA/HD, run the file scripts/evaluate_failure_prediction_heatmaps_scores_compute_all.py
For ThidEye HRL, run the file scripts/evaluate_failure_prediction_vae_on_heatmaps_compute_all.py
For SelfOracle, run the file scripts/evaluate_failure_prediction_selforacle_compute_all.py

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:

@inproceedings{2022-Stocco-ASE,
  author    = {Andrea Stocco and Paulo J. Nunes and Marcelo d'Amorim and Paolo Tonella},
  title     = {{ThirdEye}: Attention Maps for Safe Autonomous Driving Systems},
  booktitle = {Proceedings of 37th IEEE/ACM International Conference on Automated Software Engineering},
  publisher = {IEEE/ACM},
  series = {ASE '22},
  year = {2022}
}

tsigalko18/ase22