• Custom CARLA maps that mimic real-world roads and human driver behaviors from NGSim dataset (I80 and US101)
• We've trained and benchmarked policies on real-world lane change maneuvers from NGSim dataset
• We provide the source code for running NGSim-based scenarios in CARLA. Scenario interface is similar to openai gym interface

1. Download and extract CARLA (0.9.6 download link). Then, add PythonAPI wheel to your PYTHONPATH:

   export CARLA_ROOT=/path/to/your/carla/release-folder
   export PYTHONPATH=$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.6-py3.6-linux-x86_64.egg:$PYTHONPATH

2. You also need to install our asset packages with two new CARLA maps
   • Download our archive: Google Drive download link
   • Move the archive to: $CARLA_ROOT/Import
   • Ingest into CARLA release: cd Import && ./ImportAssets.sh

./CarlaUE4.sh -benchmark -fps=10

# (wait until server loads)
python example/example_roundabout_scenario_usage.py

# (wait until scenario script connects successfully, map rendering may tak a while)
python example/manual_driving.py --res 900x500

Code tested with CARLA 0.9.6.

1. Download dataset: Google Drive download link
2. Unpack: tar xf xy-trajectories.tgz
3. Adjust data_dir in source code with your path to xy-trajectories

• python example/example_scenario_usage.py - shows how to run scenario in training loop
• python example/example_replay_ngsim_in_carla.py - shows how to replay NGSim dataset in CARLA. It was used to generated GIF in this README file

Code for interfacing with NGSim dataset was based on https://github.com/Atcold/pytorch-PPUU