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https://github.com/ruddyscent/adlab-e2e
End-to-end autonomous driving simulation using Carla, ROS, and PyTorch.
https://github.com/ruddyscent/adlab-e2e
autonomous-driving carla end-to-end jupyter pytorch ros
Last synced: 2 days ago
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End-to-end autonomous driving simulation using Carla, ROS, and PyTorch.
- Host: GitHub
- URL: https://github.com/ruddyscent/adlab-e2e
- Owner: ruddyscent
- License: mit
- Created: 2024-06-07T05:16:56.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-10T18:03:57.000Z (16 days ago)
- Last Synced: 2024-09-19T20:34:46.887Z (7 days ago)
- Topics: autonomous-driving, carla, end-to-end, jupyter, pytorch, ros
- Language: Jupyter Notebook
- Homepage:
- Size: 39.1 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://carla.org/), a leading open-source simulator for autonomous driving research.
- **Runner**: Hosts a [Jupyter Notebook](https://jupyter.org/) server configured with [PyTorch](https://pytorch.org/), [Scenario Runner](https://github.com/carla-simulator/scenario_runner), and [Leaderboard](https://leaderboard.carla.org/) modules to support development and evaluation of driving algorithms.
- **ROS**: Contains [ROS](https://www.ros.org/), 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:
```dotenv
# .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`.
```dockerfile
# 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](https://github.com/carla-simulator/carla/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:
```bash
docker-compose build
```
2. **Start Containers**: Launch the containers using:
```bash
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.