ADLab-e2e

This repository provides Docker containers for running end-to-end autonomous driving simulations. Each container is defined by a Dockerfile, and you can run multiple containers simultaneously using Docker Compose. The setup leverages Carla for simulation, ROS for robotics middleware, and Jupyter for interactive development and visualization.

1. Containers Overview

The repository includes three main containers, each serving a distinct purpose in the simulation pipeline:

• Carla: Runs the Carla simulator, a leading open-source simulator for autonomous driving research.
• Runner: Hosts a Jupyter Notebook server configured with PyTorch, Scenario Runner, and Leaderboard modules to support development and evaluation of driving algorithms.
• ROS: Contains ROS, a version of the Robot Operating System, including the ROS Bridge to facilitate communication between Carla and ROS nodes.

2. Setup Instructions

Follow these steps to set up and run the simulation environment:

2.1 Environment Configuration

Create a .env file in the root directory of your repository to configure environment variables required by Docker Compose:

# .env file
UID=1000 # User ID of the current user
CARLA_GPU_DEVICES=0 # GPU devices assigned to the Carla container
CARLA_RPC_PORT=2000 # Port number for Carla clients to connect (default: 2000)

JUPYTER_GPU_DEVICES=0 # GPU device assigned to the Jupyter container
JUPYTER_PORT=8888     # Port number for the Jupyter server (default: 8888)
JUPYTER_TOKEN=letmein # Authentication token for the Jupyter server

2.2 Carla Version

To set the version of Carla, modify the CARLA_VER variables in both the compose.yml. Ensure that the CARLA_VER value is consistent across containers. The default version is 0.9.15.

# Example: compose.yml
args:
   - CARLA_VER=0.9.15

2.3 Additional Carla Maps

To enhance your simulation environment with additional maps, download the desired map files from Carla's releases page. Place these files in the root directory of your repository.

3. Running the Simulation

1. Build Containers: Use Docker Compose to build all containers by running:

   docker-compose build
   

2. Start Containers: Launch the containers using:

   docker-compose up
   

   This command will start the Carla simulator, Jupyter Notebook server, and ROS environment, connecting all components as configured.

3. Access Jupyter Notebooks: Open your web browser and go to http://localhost:8888 (or the port specified in your .env file). Use the token specified in JUPYTER_TOKEN to log in.

4. Development and Experimentation

• Carla Simulator: The simulator is accessible on the port specified by CARLA_RPC_PORT and can be interacted with using Carla clients.
• Jupyter Notebooks: Use Jupyter Notebooks for running experiments, analyzing data, and visualizing simulation results. The setup includes PyTorch support for deep learning model development.
• ROS Integration: Leverage ROS nodes to create complex robotic systems and test autonomous driving algorithms in simulated environments.

5. Troubleshooting

• GPU Configuration: Ensure that your Docker setup is configured to use NVIDIA GPUs. You may need to install the NVIDIA Container Toolkit if not already set up.
• Networking Issues: If containers cannot communicate, check the network settings in your compose.yml and ensure that the correct ports are open and not blocked by firewalls.

6. Contributing

Contributions are welcome! Please open an issue or submit a pull request to contribute to this project.

7. License

This project is licensed under the MIT License. See the LICENSE file for more details.