By Peiyun Hu, Aaron Huang, John Dolan, David Held, and Deva Ramanan

You can find our paper on CVF Open Access. If you find our work useful, please consider citing:

@inproceedings{hu2021safe,
  title={Safe Local Motion Planning with Self-Supervised Freespace Forecasting},
  author={Hu, Peiyun and Huang, Aaron and Dolan, John and Held, David and Ramanan, Deva},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12732--12741},
  year={2021}
}

• Download nuScenes dataset, including the CANBus extension, as we will use the recorded vehicle state data for trajectory sampling. (Tip: the code assumes they are stored under /data/nuscenes.)
• Install packages and libraries (via conda if possible), including torch, torchvision, tensorboard, cudatoolkit-11.1, pcl>=1.9, pybind11, eigen3, cmake>=3.10, scikit-image, nuscenes-devkit. (Tip: verify location of python binary with which python.)
• Compile code for Lidar point cloud ground segmentation under lib/grndseg using CMake.

• Run preprocess.py to generate ground segmentations
• Run precast.py to generate future visible freespace maps
• Run rasterize.py to generate BEV object occupancy maps and object "shadow" maps.

Refer to train.py.

Refer to test.py.

Thanks @tarashakhurana for help with README.