lgsvl/lanefollowing
📦 Open Source Projectlgsvl
End-to-end lane following autonomous driving model integrated with the SVL Simulator and ROS2.
The lgsvl/lanefollowing repository offers a practical implementation of behavioral cloning for autonomous driving. By leveraging the SVL Simulator, users can generate synthetic driving data to train neural networks that predict steering angles based on visual input. The project architecture focuses on end-to-end learning, where the model learns the mapping from raw image pixels to vehicle control commands without explicit feature engineering. Key technical components include the use of TensorFlow and Keras for building convolutional and recurrent neural network architectures, and ROS2 for real-time communication between the perception model and the vehicle control stack. This setup allows for rapid prototyping of driving policies in a safe, virtual environment, making it an excellent resource for testing how deep learning models handle lane-keeping tasks under varying simulated conditions.
💡Highlights
- ├─ROS2-integrated driving pipeline
- ├─End-to-end behavioral cloning
- └─CNN/RNN-based steering control
🎯For
- ├─Autonomous Driving Researchers
- ├─Robotics Engineers
- └─AI Students