This repo consists of the source code for FusED and the examples of the fusion errors it has found.
We show the following fusion error examples. The left window of each pair shows an accident caused by the fusion component and the right window shows the accident would be avoided if the fusion method is replaced to "best-sensor fusion"(defined in the paper) during the pre-crash period in a counter-factual world.
1.OpenPilot (with DEFAULT - the default fusion method as the initial fusion method):
The ego car avoids its collision with the green vehicle ahead after the replacement.
2.OpenPilot (with DEFAULT - the default fusion method as the initial fusion method):
The ego car avoids its collision with the green vehicle moving out after the replacement.
3.OpenPilot (with MATHWORKS - a kalman-filter based fusion method as the original fusion method):
The ego car avoids its collision with the police car cutting in from the right lane after the replacement.
4.OpenPilot (with MATHWORKS - a kalman-filter based fusion method as the original fusion method):
The ego car avoids its collision with the red vehicle cutting in from the right lane after the replacement.
- Monitor (i.e., due to the limitation of OpenPilot, the simulation can only run on a machine with a monitor/virtual monitor)
- OS: Ubuntu 20.04
- CPU: at least 6 cores
- GPU: at least 6GB memory
- Openpilot 0.8.5 (customized)
- Carla 0.9.11
~(home folder)
├── openpilot
├── Documents
│ ├── self-driving-cars (created by the user manually)
│ │ ├── FusED
│ │ ├── carla_0911_rss
Note: one can create link for these folders at these paths if one cannot put them in these paths.
In ~,
git clone https://github.com/AIasd/openpilot.git
In ~/openpilot,
./tools/ubuntu_setup.sh
In ~/openpilot, compile Openpilot
scons -j $(nproc)
Make sure the python path is set up correctly through pyenv, in particular, run
which python
One should see the following:
~/.pyenv/shims/python
Otherwise, one needs to follow the displayed instructions after running
pyenv init
clang 10 is needed. To install it, run
sudo apt install clang
Your environment needs to support opencl 2.0+ in order to run scons successfully (when using clinfo, it must show something like "your OpenCL library only supports OpenCL <2.0+>")
In ~/Documents/self-driving-cars,
curl -O https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.11_RSS.tar.gz
mkdir carla_0911_rss
tar -xvzf CARLA_0.9.11_RSS.tar.gz -C carla_0911_rss
In ~/Documents/self-driving-cars/carla_0911_rss/PythonAPI/carla/dist,
easy_install carla-0.9.11-py3.7-linux-x86_64.egg
In ~/Documents/self-driving-cars/carla_0911_rss,
curl -O https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/AdditionalMaps_0.9.11.tar.gz
mv AdditionalMaps_0.9.11.tar.gz Import/
and then run
./ImportAssets.sh
In ~/Docuements/self-driving-cars,
git clone https://github.com/FusionFuzz/FusED.git
In /Docuements/self-driving-cars/FusED,
pip3 install -r requirements.txt
Install pytorch
pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
To check everything is set up correctly and FusED can run successfully, we first run one scenario.
In ~/Docuements/self-driving-cars/FusED,
python ga_fuzzing.py --simulator carla_op --n_gen 1 --pop_size 1 --algorithm_name nsga2 --has_run_num 1 --episode_max_time 200 --only_run_unique_cases 0 --objective_weights 1 0 0 0 -1 -2 0 -m op --route_type 'Town06_Opt_forward'
Two windows should pop up with one showing the OpenPilot running in the CARLA simulator.
In the command line, the program should ends with We have found (x) bugs in total. before showing a series of "kill server" messages.
Note since only one randomly sampled scenario is run here, it might not be a fusion error. To guarantee the finding of fusion errors, see the Run Fuzzing subsection next under Detailed Description where 500 scenarios have been run.
The main results are the numbers of (distinct) fusion errors found using FusED and the baseline methods. Since the fuzzing process has randomness, the numbers won't be exactly the same across runs but they should be similar to the paper's reported ranges for each setting.
In ~/Docuements/self-driving-cars/FusED,
python ga_fuzzing.py --simulator carla_op --n_gen 10 --pop_size 50 --algorithm_name nsga2 --has_run_num 500 --episode_max_time 200 --only_run_unique_cases 0 --objective_weights 1 0 0 0 -1 -2 0 -m op --route_type 'Town06_Opt_forward'
Depending on the CPU one uses, this process can take from 12 hours to 24 hours.
The default algorithm is GA-Fusion.
For GA, replace --objective_weights 1 0 0 0 -1 -2 0 with --objective_weights 1 0 0 0 -1 0 0.
For Random, replace --algorithm_name nsga2 with --algorithm_name random.
The failure cases can be found in ~/Docuements/self-driving-cars/ADFuzz/run_results_op/<algorithm_name>/<route_type>/<route_type>/<m>/<folder_with_starting_time>/bugs. In particular, under each subfolder, the folder front contains all the front camera images.
Move all the subfolders in ~/Docuements/self-driving-cars/ADFuzz/run_results_op/<algorithm_name>/<route_type>/<route_type>/<m>/<folder_with_starting_time>/bugs to ~/openpilot/tools/sim/op_script/rerun_folder, then in ~/openpilot/tools/sim/op_script,
python rerun_carla_op.py -p rerun_folder -m2 best_sensor -w 2.5
Depending on the CPU one uses and the number of collisions the fuzzing process has found, this process can take from 2 hours to 4 hours.
The rerun process of all the identified fusion errors during the rerun process can be found in ~/openpilot/tools/sim/op_script/rerun_op/<algorithm_name>/<route_type>/<route_type>/<m>/<folder_with_starting_time>/non_bugs. They are considered fusion errors because after the replacement of the fusion method, they avoid the original collisions.
In openpilot/tools/sim/op_script,
python trajectory_analysis.py -p rerun_folder -f <fusion folder>
where <fusion folder> should be set to the folder contains the fusion errors identified during the rerun process, i.e., ~/openpilot/tools/sim/op_script/rerun_op/<algorithm_name>/<route_type>/<route_type>/<m>/<folder_with_starting_time>/non_bugs.
The command line should show a line cur X filtering: a -> b where a represents the number of fusion errors found and b represents the number of distinct fusion errors found.
If you use the project in your work, please consider citing it with:
@misc{https://doi.org/10.48550/arxiv.2109.06404,
doi = {10.48550/ARXIV.2109.06404},
url = {https://arxiv.org/abs/2109.06404},
author = {Zhong, Ziyuan and Hu, Zhisheng and Guo, Shengjian and Zhang, Xinyang and Zhong, Zhenyu and Ray, Baishakhi},
keywords = {Robotics (cs.RO), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
This repo uses pymoo as the underlying framework for Multi-objective Optimization.