CARLA excels in several application scenarios:
1. algorithm development: Train perception and planning models by simulating sensor data such as LIDAR and camera.
2. Traffic Scenario Testing: Complex environments (e.g., severe weather, accident scenarios) can be simulated to verify robustness.
3. Education and training: Colleges and universities use CARLA to teach the fundamentals of autonomous driving, and students are able to quickly practice control algorithms through Python APIs.
4. Multi-intelligence research: Support for multi-vehicle cooperative or competitive driving experiments (e.g., fleet communication or gaming scenarios). the open-source nature of CARLA further extends its applicability.
This answer comes from the articleCARLA: an open source autonomous driving research simulatorThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil