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Conditional Affordance Learning

Installation

# install anaconda2 if you don't have it yet
wget https://repo.continuum.io/archive/Anaconda2-4.4.0-Linux-x86_64.sh
bash Anaconda2-4.4.0-Linux-x86_64.sh
source ~/.profile
# or use source ~/.bashrc - depending on where anaconda was added to PATH as the result of the installation
# now anaconda is assumed to be in ~/anaconda2

Now we will:

  1. create a conda environment named CAL and install all dependencies
  2. download the binaries for CARLA version 0.8.2 [CARLA releases]
  3. download the model weights
git clone https://github.com/xl-sr/CAL.git
cd CAL

# create conda environment
./setup.sh

# run download script
./download_binaries_and_models.sh

Run the Agent

In CARLA_0.8.2/ start the server with for example: (see the CARLA documentation for details)

./CarlaUE4.sh Town01 -carla-server -windowed -benchmark -fps=20 -ResX=800 - ResY=600

Open a second terminal, cd into CAL/PythonClient/ and run:

python driving_benchmark.py -c Town02 -v -n test

This runs the basic_experiment benchmark. '-n' is the naming flag (in this example the run is named "test"). If you want to run the CORL 2017 benchmark you need to run

python driving_benchmark.py -c Town02 -v -n test --corl-2017

If you want to continue an experiment, you can add the 'continue-experiment' flag.

Paper

If you use this implementation, please cite our CoRL 2018 paper.

Conditional Affordance Learning for Driving in Urban Environments.
Sauer, Axel and Savinov, Nikolay and Geiger, Andreas.
CORL 2018 [PDF]

@INPROCEEDINGS{Sauer2018CORL,
  author={Sauer, Axel and Savinov, Nikolay and Geiger, Andreas},
  title={Conditional Affordance Learning for Driving in Urban Environments},
  booktitle={Conference on Robot Learning (CoRL)},
  year={2018}
}