Bird-eye's view for CARLA simulator
Bird-eye's view for CARLA
|Freeway-oriented shape||Learning By Cheating-like shape|
(GIFs above present feature maps after applying
During our research we found a very inspiring paper called Learning By Cheating. Bird-eye's view is made specifically to learn faster thanks to much simpler, 2D world representation (cheating oracle) which we think fits well in Reinforcement Learning setup.
This repository is an almost complete reimplementation that gives better performance and compatibility with most recent versions of CARLA. You can use it out-of-the-box as input for your model, and if necessary convert and visualize into RGB.
- one-hot 3D feature map (8x2D layers, each representing other entities, e.g. road layer, pedestrians layer) - made specifically to feed your CNN
- feature map can be converted to an RGB image
- layers can be easily removed
- caching mechanism for static layers like: roads and lanes
- using OpenCV rendering (efficient, multi-threading friendly) instead of slow Pygame method
- huge FPS speedup thanks to restricted rendering (only agent's surroundings, not whole map)
- all CARLA maps are supported out-of-the-box, custom maps with valid OpenDrive file made in RoadRunner are also supported
- current implementation is specifically adjusted for highway scenarios (prolonged shape), but other shapes and crops are easy to implement
pip install carla-birdeye-view
How to run
Make sure that
PYTHONPATH env variable contains CARLA distribution egg, so that
carla package can be imported.
# Launch server instance ./CarlaUE4.sh # (optional) For CARLA 0.9.8+ you may get additional performance improvement with this python PythonAPI/util/config.py --no-rendering # Preview while cruising on autopilot (birdview/__main__.py) birdview-demo
Basic code usage
from carla_birdeye_view import BirdViewProducer birdview_producer = BirdViewProducer( client, target_size=PixelDimensions(width=birdview.DEFAULT_WIDTH, height=birdview.DEFAULT_HEIGHT), pixels_per_meter=4, ) # Input for your model - call it every simulation step # returned result is np.ndarray with ones and zeros of shape (8, height, width) birdview = birdview_producer.produce(agent_vehicle=agent) # Use only if you want to visualize # produces np.ndarray of shape (height, width, 3) rgb = BirdViewProducer.as_rgb(birdview)
Contribution and feedback
We'd :heart: to collct any feedback, issues and pull requests!
Project born at deepsense.ai, made by:
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