Readme of Supplementary Data File

This is the code repository for article "Continuous Improvement of Self-Driving Cars using Dynamic Confidence-Aware Reinforcement Learning"

1. Structure

The files are structured as follows, where the note in the brackets briefly explains the content:

.
+-- Simulation_testing [Codes and Data in the article for Experiment 1 and 2]
|   +-- Simulation1
|   |   +-- test_DCARL.py [Run the results in Simulation1]
|   +-- Simulation2
|   |   +-- test_DCARL.py [Run the results in Simulation2]
|   +-- Simulation_Data_Collection
|   |   +-- Data_Sampling
|   |   |   +-- data_sampling.py [Collect More Simulated Data in a Distribution]
|   |   +-- Data_From_Carla
|   |   |   +-- test_value_collect.py [Collect More Simulated Data from CARLA]
+-- Field_Testing [Codes and Data in the article for Experiment 3]
|   +-- Software_and_Raw_Data_on_Self-Driving_Vehicle
|   |   +-- software [Codes on self-driving vehicles. It's a copy of CLAP framework which refer to https://gitlab.com/umvdl/zzz/zzz/-/tree/dev/xiaopengG3]
|   |   +-- run_docker.sh [DEMO codes]
|   |   +-- demo.rviz [Visual interface for the codes]
|   +-- Simulation1
|   |   +-- DrawData.m [Run for the results of scenario 1 in field testing]
|   +-- Simulation2
|   |   +-- DrawData.m [Run for the results of scenario 2 in field testing]
|   +-- Simulation3
|   |   +-- DrawData.m [Run for the results of scenario 3 in field testing]

2. Light DEMO Codes for Simulation 1 and 2

This DEMO shows the results of simulation 1 and 2. The function is to estimate the confidence value of different policies and decide the final trajectory.

2.1 Dependencies

2.2 Installation

The package is written in Python 3. The usage of the Anaconda Python distribution is recommended.

After you have installed Python 3.6.8 or above, open a terminal in the Supplementary Data File and execute the following command to install the software (including the dependencies):

pip install -r ./requirements.txt

2.3 Usage

Run the results for simulation 1:

python .\Simulation_testing\Simulation_1\test_DCARL.py

Run the results for simulation 2:

python .\Simulation_testing\Simulation_2\test_DCARL.py

2.4 More Data for evaluation

You can use more data for evaluation. We provide two ways for data collection

2.4.1 Sampling-based Data Collection

Sampling the data from the true value of the state-action pair by running:

python .\Simulation_testing\Simulation_Data_Collection\Data_Sampling\data_sampling.py

2.4.2 CARLA-based Data Collection

Sampling the data from the CARLA simulation requires several steps:

Download CARLA>=0.9.13
Run CARLA in the downloaded CARLA Folder
```
.\CarlaUE4.sh
```

Then, open another terminal and run

python .\Simulation_testing\Simulation_Data_Collection\Data_From_Carla\data_value_collect.py

2.4.3 Instruction for other data:

A single data point should be formulated into {state, action, cumulative rewards} Then, setting the original policy's action as the action 0.

After that, when given a state, the DCARL agent can generate an action according to the collected data.

3. Field Testing Data and Codes

We provide the source codes on the autonomous vehicle and the data for three scenarios.

3.1 Dependencies

This demo requires ubuntu 18.04 and x86 platform.
docker>=20.10.16
ROS melodic
rviz

3.2 Installation

3.2.1 Install ros

Official tutotial can be found here: https://wiki.ros.org/melodic/Installation/Ubuntu

Setup your sources.list

sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'

Set up your keys

curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | sudo apt-key add -

update apt source
```
sudo apt update
```

install ros-melodic-desktop-full

sudo apt install ros-melodic-desktop-full

activate ros environment
```
source /opt/ros/melodic/setup.bash
```
rviz is default installed.
This install takes about 20 minutes.

3.2.2 Install docker

Docker is default on ubuntu. If docker is not available, following this official tutorial to install docker on Linux: https://docs.docker.com/engine/install/ubuntu/.
This install takes about 10 minutes.

3.2.3 Download docker image

The running environment is provided by docker image. Download the image from open docker hub.
```
docker pull thuvehicle/dcarl_docker:v1
```

3.2.4 Download field data records

Download the field testing rosbag data from https://drive.google.com/file/d/1tTAyAlWsg4ZoeJVPpALbaXdlqhnm8P6X/view?usp=sharing.

Unzip the file and copy the rosbags to desired location.

unzip DCARL_data.zip
cd data
cp -r rosbag/ PATH_TO_PROJECT/supplementary_data_file/Field_testing/Software_and_Raw_Data_on_Self-Driving_Vehicle/software

3.3 Usages

3.3.1 Steps

Run the docker image, and the source code is automatically mapped

cd PATH_TO_PROJECT/supplementary_data_file/Field_testing/Software_and_Raw_Data_on_Self-Driving_Vehicle
bash run_docker.sh

Now the terminal is inside the docker, enter the source location
```
cd /zzz
```

Now, the running requirements is satisfied. We will use tmux to avoid opening multiple terminal.

Type tmux in the terminal, and enter the tmux environment

Type ctrl+b, and press c to open a new terminal in tmux, we need 4 termials in total. We can see the number of opened terminal at bottom. The ctrl+b is a triggering operation in tmux. Before starting any operation of tmux, ctrl+b is required,
Type ctrl+b, and press number to swtich between different terminals.
In first tmux terminal, type roscore to start ros.
In second tmux terminal, we need to start the RL agent server by
```
cd src/tools/DCARL/
python3 DCARL_agent.py
```
In third tmux terminal, start the planner by
```
launch_planner.sh
```
In forth tmux terminal, play the rosbag. Not the number after script, it indicates the number of case selected. 1, 2, and 3 is available.
```
launch_rosbag.sh 1
```
At last, open a new terminal outside of docker to run RVIZ. We don't run rviz in docker because it may cause subtle bugs on different machine.
```
rviz -d demo.rviz
```
Need to notify that it's required to restart the code in step 8 and step 9 if you want to switch between different bags by running launch_rosbag.sh 1(2 or 3)

3.3.2 Expected run time

The operation before running takes about 10 minutes. To see all the demo cases requires 5 minutes.

3.3.3 Expected output

The planning outputs trajectories online observing data record from rosbag. We can observed safe and reliable trajectories in RVIZ under different scenarios.

3.4 Collected Data Display for three Example Cases

To redraw the Fig. 5 in the main text, we provide the source data of these cases and the codes for figures.

These codes are written in Matlab>=R2020b.

Run the following code in Matlab for case 1:

\Field_testing\Scenario1\DrawData.m

Run the following code in Matlab for case 2:

\Field_testing\Scenario2\DrawData.m

Run the following code in Matlab for case 3:

\Field_testing\Scenario3\DrawData.m

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