add PG
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@@ -2,5 +2,20 @@
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PG
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PG
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############
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############
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Known Issues
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The PG scenarios are collected by running simulation and record the episodes in MetaDrive simulator.
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The name PG refers to Procedural Generation, which is a technique used to generate maps.
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When a map is determined, the vehicles and objects will be spawned and actuated according to a hand-crafted rules.
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Build PG Database
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===================
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If MetaDrive is installed, there is no any further steps required to build the database. Just run the following
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command to generate, i.e. 1000 scenarios::
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python -m scenarionet.convert_pg -d /path/to/pg_database --num_scenarios 1000
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Known Issues: PG
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==================
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==================
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N/A
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@@ -42,7 +42,7 @@ And place the downloaded tfrecord file to a folder. Let's call it ``exp_waymo``
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Likewise, place all downloaded tfrecord files to the same folder.
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Likewise, place all downloaded tfrecord files to the same folder.
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3. Build Database
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3. Build Mini Database
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Run the following command to extract scenarios in ``exp_waymo`` to ``exp_converted``::
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Run the following command to extract scenarios in ``exp_waymo`` to ``exp_converted``::
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@@ -24,7 +24,8 @@ The dataset includes:
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using new Lyft data.
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using new Lyft data.
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Known Issues: Lyft
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===================
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Known Issues
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N/A
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#############
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@@ -2,5 +2,7 @@
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nuPlan
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nuPlan
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#############################
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#############################
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Known Issues
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Known Issues: nuPlan
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==================
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======================
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N/A
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@@ -2,5 +2,7 @@
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nuScenes
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nuScenes
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#############################
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#############################
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Known Issues
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Known Issues: nuScenes
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==================
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=======================
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N/A
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@@ -26,7 +26,7 @@ The dataset includes:
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- Adjusted some road edge boundary height estimates
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- Adjusted some road edge boundary height estimates
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1. Install requirements
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1. Install Waymo Toolkit
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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First of all, we have to install the waymo toolkit and tensorflow::
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First of all, we have to install the waymo toolkit and tensorflow::
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@@ -40,7 +40,7 @@ First of all, we have to install the waymo toolkit and tensorflow::
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.. note::
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.. note::
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This package is only supported on Linux platform.
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This package is only supported on Linux platform.
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2. Download Raw Data
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2. Download TFRecord
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Waymo motion dataset is at `Google Cloud <https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_2_0>`_.
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Waymo motion dataset is at `Google Cloud <https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_2_0>`_.
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@@ -72,7 +72,7 @@ The downloaded data should be stored in a directory like this::
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└── ...
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└── ...
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3. Build Database
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3. Build Waymo Database
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Run the following command to extract scenarios in any directory containing ``tfrecord``.
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Run the following command to extract scenarios in any directory containing ``tfrecord``.
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@@ -82,7 +82,7 @@ Here we take converting raw data in ``training_20s`` as an example::
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Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work.
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Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work.
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Known Issues
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Known Issues: Waymo
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==================
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=====================
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N/A
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N/A
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