scenarionet/documentation/waymo.rst

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Waymo
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| Website: https://waymo.com/open/
| Download: https://waymo.com/open/download/
| Paper: https://arxiv.org/abs/2104.10133

The dataset includes:

- 103,354, 20s 10Hz segments (over 20 million frames), mined for interesting interactions
- 574 hours of data
- Sensor data
    - 4 short-range lidars
    - 1 mid-range lidar
- Object data
    - 10.8M objects with tracking IDs
    - Labels for 3 object classes - Vehicles, Pedestrians, Cyclists
    - 3D bounding boxes for each object
    - Mined for interesting behaviors and scenarios for behavior prediction research, such as unprotected turns, merges, lane changes, and intersections
    - 3D bounding boxes are generated by a model trained on the Perception Dataset and detailed in our paper: Offboard 3D Object Detection from Point Cloud Sequences
      Map data
- 3D map data for each segment
- Locations include: San Francisco, Phoenix, Mountain View, Los Angeles, Detroit, and Seattle
- Added entrances to driveways (the map already Includes lane centers, lane boundaries, road boundaries, crosswalks, speed bumps and stop signs)
- Adjusted some road edge boundary height estimates


1. Install Waymo Toolkit
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

First of all, we have to install the waymo toolkit and tensorflow::

    pip install waymo-open-dataset-tf-2-11-0
    pip install tensorflow==2.11.0

.. note::
    This package is only supported on Linux platform.
    `waymo-open-dataset` may degrade numpy, causing conflicts with cv2.
    A workaround is ``pip install numpy==1.24.2``

2. Download TFRecord
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Waymo motion dataset is at `Google Cloud <https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_2_0>`_.
For downloading all datasets, ``gsutil`` is required.
The installation tutorial is at https://cloud.google.com/storage/docs/gsutil_install.

After this, you can access all data and download them to current directory ``./`` by::

    gsutil -m cp -r "gs://waymo_open_dataset_motion_v_1_2_0/uncompressed/scenario" .

Or one just can download a part of the dataset using command like::

    gsutil -m cp -r "gs://waymo_open_dataset_motion_v_1_2_0/uncompressed/scenario/training_20s" .

The downloaded data should be stored in a directory like this::

    waymo
    ├── training_20s/
    |   ├── training_20s.tfrecord-00000-of-01000
    |   ├── training_20s.tfrecord-00001-of-01000
    |   └── ...
    ├── validation/
    |   ├── validation.tfrecord-00000-of-00150
    |   ├── validation.tfrecord-00001-of-00150
    |   └── ...
    └── testing/
        ├── testing.tfrecord-00000-of-00150
        ├── testing.tfrecord-00001-of-00150
        └── ...


3. Build Waymo Database
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Run the following command to extract scenarios in any directory containing ``tfrecord``.
Here we take converting raw data in ``training_20s`` as an example::

    python -m scenarionet.convert_waymo -d /path/to/your/database --raw_data_path ./waymo/training_20s --num_files=1000

Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work.

Known Issues: Waymo
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
N/A