CARLA provides a complete tool chain for perceptual algorithm training:
- Sensor Configuration: Deployment of RGB cameras on vehicles (
sensor.camera.rgb), semantic segmentation camera (sensor.camera.semantic_segmentation) and LIDAR (sensor.lidar.ray_cast). - Data acquisition: using
camera.listen(lambda image: image.save_to_disk())Automatic saving of image frames, HDF5 format is recommended for storing large-scale datasets. - Scenario diversity: dynamic adjustment of weather (rain and fog intensity), lighting (sun azimuth), and traffic density via Python API (
TrafficManager). - Labeling automation: semantic labels and depth maps automatically generated by CARLA can be directly used for supervised learning.
- Integration with TensorFlow/PyTorch: Feed data directly to the training pipeline via ROS bridge or custom Python data loader.
This answer comes from the articleCARLA: an open source autonomous driving research simulatorThe
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