TM-fuzzer

An innovative open-source framework designed to improve the safety and reliability of Autonomous Vehicles (AVs) through enhanced scenario-based testing.

Developed using insights from cutting-edge research, TM-fuzzer leverages unique techniques for traffic management and scenario diversity to identify critical scenarios that expose potential vulnerabilities in ADS.

Key Features

NPC Traffic Management: TM-fuzzer introduces a novel method for Non-Player Character (NPC) traffic management, which includes dynamic insertion, deletion, and management of NPCs. This approach increases interactions between NPCs and the ego vehicle, enhancing the complexity of test scenarios.

Diversity Analysis through Clustering: We employ clustering analysis to ensure the diversity of test scenarios. By analyzing the features from vehicle trajectories in previous accidents, TM-fuzzer optimizes scenario selection to expose new and unique ADS defects.

Efficient Scenario Searching: The framework dynamically generates NPCs and utilizes advanced clustering techniques to create a diverse range of realistic traffic situations, significantly improving the detection rate of ADS vulnerabilities.

Testing environment

The following environment was used to perform the testing for this project:

• Hardware
  • CPU: Intel(R) Xeon(R) Gold 5120 CPU @ 2.20GHz
  • GPU: Tesla V100-32GB (x2)
  • RAM: 128 GB
• OS & SW
  • Ubuntu 18.04.6 LTS with Linux 5.4.0-139-generic
    • This is strictly required by Autoware, one of our test targets
  • Python 3.6.9

In addition, CARLA (the simulator we use) requires the following: (based on https://carla.readthedocs.io/en/0.9.13/build_linux/)

• Ubuntu 18.04
• 30GB disk space
• An adequate GPU
• Two TCP ports and good internet connection

Installation

Before installing TM-fuzzer, make sure docker and nvidia-docker2 has been installed properly in your machine.

1. Install CARLA 0.9.13

Installing

Please refer to the official CARLA installation guide: Installing Carla from docker

Or just pull by:

docker pull carlasim/carla:0.9.13

Quick-running Carla

Carla can be run using a wrapper script run_carla.sh. If you have multiple GPUs installed, it is recommended that you "pin" Carla simulator to one of the GPUs (other than #0). You can do that by opening run_carla.sh and modifying the following:

-e NVIDIA_VISIBLE_DEVICES={DESIRED_GPU_ID} --gpus 'device={DESIRED_GPU_ID}

To run carla simulator, execute the script:

$ ./run_carla.sh

It will run carla simulator container, and name it carla-${USER} .

To stop the container, do:

$ docker rm -f carla-${USER}

2. Install carla-autoware docker

Please refer to the official carla-autoware installation guide, and in order to fit our own mechine (which works without external network access capabilities), and make it work in TM-fuzzer, we make some modifications in our own forks.

Our Modifications:

• Add proxy server, nameserver, ros source in the dockerfile, which can be deleted if you don't need them.
• Add our own entrypoint.sh so we can run simulation directly after the container is started.
• Add a shell script pub_initialpose.py so we can easily change the initial pose of the ego vehicle.
• Add a shell script reload_autoware.sh so we can easily reload the simulation without restarting the container.
• Add some camera in objects.json for recording the simulation.
• Upgraded the carla version to 0.9.13, file changed in update_sim_code.patch, carla-autoware-agent/launch/carla_autoware_agent.launch

So, first clone the carla-autoware repo modified by us:

git clone https://github.com/cfs4819/carla-autoware/tree/TMfuzz

then make some modifications in Dockerfile depends on your mechine.

Then, download the additional files

cd carla-autoware/
git clone https://bitbucket.org/carla-simulator/autoware-contents.git

Last, build the carla-autoware repo

./build.sh

3.Installing ROS-melodic

ROS is required on the host in order for TM-Fuzzer to communicate with the Autoware container.

sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
sudo apt install curl
curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | sudo apt-key add -
sudo apt update
sudo apt install ros-melodic-desktop-full
source /opt/ros/melodic/setup.bash

4.Installing other dependent environments

pip install -r requirements.txt

Usage

1. Prepare environment

mkdir -p /tmp/fuzzerdata
sudo chmod 777 /tmp/fuzzerdata
mkdir -p bagfiles
sudo chmod 777 $HOME/.Xauthority
source /opt/ros/melodic/setup.bash

2. Run fuzzing

• Testing Autoware

cd ./TM-fuzzer/script
./test.sh autoware 0.4 3 3600

• Testing Behavior Agent

cd ./TM-fuzzer/script
./test.sh behavior 0.4 3 3600

• Outputs (bugs, mutated test inputs, metadata, ...) will be stored in data.
• You can change the test details by modifying the parameters after ./test.sh. The second parameter represents the vehicle density in the traffic flow, the third parameter represents the test map number (from 1 to 5), and the fourth parameter represents the total test time (seconds).
• Check ./TM-fuzzer/fuzzer.py --help for further instructions.

Vioaltion details

Category	ID	Summary of Violation
A	A-1	Inaccurate stop at destination causing overshoot.
A	A-2	Misinterpretation of destination across lanes.
A	A-3	Erroneous permanent halt on slopes.
A	A-4	Misjudgment of road surface as obstacle on declines.
A	A-5	Difficulty in exiting roundabouts correctly.
A	A-6	Misjudgment at intersections affecting route continuity.
A	A-7	Excessive turning angles causing lane boundary crossings.
A	A-8	Insufficient turning radius leading to collisions with walls.
B	B-1	Delayed braking leading to rear-end collisions.
B	B-2	Non-detection of non-motorized entities leading to collisions.
B	B-3	Collisions during lane changes with large vehicles.
B	B-4	Reckless lane changes causing collisions.
B	B-5	Collisions on slopes due to failed vehicle detection ahead.
B	B-6	Entry collisions in roundabouts.
B	B-7	Inappropriate lane changes in roundabouts leading to collisions.
B	B-8	Lane marking violations at T-intersections to avoid merging vehicles.
B	B-9	Collisions during merging at T-intersections with original lane vehicles.
B	B-10	Collisions during merging at T-intersections with new lane vehicles.
B	B-11	Collisions with straight-moving vehicles at crossroads.
B	B-12	Collisions during unsignaled left turns with oncoming vehicles.
B	B-13	Collisions across opposite lanes during unsignaled left turns.
B	B-14	Collisions with vehicles making diagonal left turns at intersections.
C	C-1	Incorrect route planning in congested traffic leading to unreachable destination.
C	C-2	Entering opposing lane during traffic congestion.
C	C-3	Loss of vehicle control due to failure to issue next command in complex conditions.
C	C-4	Overly cautious driving preventing roundabout exit in complex conditions.
C	C-5	Incorrect overtaking judgments leading to collisions with curbs.
C	C-6	Overly conservative driving at intersections preventing unsignaled left turns.
C	C-7	Incorrect maneuvers during unsignaled left turns leading to collisions with curbs.

Citation

If you use TM-fuzzer in your research, please cite our paper:

Shenghao Lin, Fansong Chen, Laile Xi et al. TM-fuzzer: fuzzing autonomous driving systems through traffic management, 03 April 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4185312/v1]

BibTeX:

@article{lin2024tm,
  title={TM-fuzzer: fuzzing autonomous driving systems through traffic management},
  author={Lin, Shenghao and Chen, Fansong and Xi, Laile and Wang, Gaosheng and Xi, Rongrong and Sun, Yuyan and Zhu, Hongsong},
  year={2024}
}

License

This project is licensed under the MIT License—see the LICENSE file for details.