DivitMittal/CARLA-Autonomous-Driving
Via high-fidelity CARLA vehicle simulator & deep semantic segmentation, data from RGBA cameras and LiDAR sensors are combined to achieve comprehensive environmental awareness
This system helps autonomous vehicle researchers and developers design and test self-driving algorithms in a realistic virtual environment. It takes data from simulated cameras and LiDAR sensors and uses deep learning to understand the vehicle's surroundings. The output is a robust perception system that identifies objects, lane markings, and environmental features, ready for integration into autonomous driving systems.
Use this if you are developing or researching autonomous driving systems and need a high-fidelity simulator to test perception, planning, and control algorithms using multi-modal sensor data.
Not ideal if you are looking for a pre-trained, production-ready autonomous driving system for real-world deployment without further development.
Stars
19
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 15, 2026
Commits (30d)
0
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