Imitation Learning

Carla has provided trained end-to-end imitation learning model code on their GitHub IL repository.

Prerequisites

  • Ubuntu 16.04
  • Carla
  • Python 3

Setup

  1. Ensure that Carla is running as expected.

  2. Go to the PythonClient directory and clone the imitation learning code:

    git clone https://github.com/carla-simulator/imitation-learning.git

  3. The Imitation Learning code uses TensorFlow. Installation instructions can be found on the official documentation, or run the following commands:

    sudo apt update
sudo apt install python3-dev python3-pip

pip3 install tensorflow-gpu --user # for gpu version
pip3 install tensorflow --user # for cpu version (choose only one!)

Note

The Imitation Learning code was tested on TensorFlow version 1.4.0, but other versions should be compatible as well.

  1. Test your TensorFlow installation before proceeding. Run python3 and then:

    import tensorflow

If no errors pop up, go ahead with the next step. Otherwise, lookup the error message on the internet for possible solutions. Note that most issues with TensorFlow installations happen due to issues with CUDA and cuDNN libraries and version mismatch.

  1. One terminal one, run the carla server as decribed before:

    ./CarlaUE4.sh -carla-server /Game/Maps/Town02 -benchmark -fps=10 -windowed -ResX=800 -ResY=600

  2. On the other terminal run the following command:

    python3 PythonClient/imitation-learning/run_CIL.py -c Town02

If all goes, well, this command will go thorough all the scenarios described in the benchmark file (PythonClient/carla/driving_benchmark/experiment_suites/corl_2017.py). The poses mentioned in the corl_2017.py file refer to an array of various starting and ending positions of the car. These positions can be visualized by running the view_start_positions.py file.

References:

  • [1] F. Codevilla, M. Müller, A. López, V. Koltun, and A. Dosovitskiy, “End-to-end Driving via Conditional Imitation Learning,” arXiv:1710.02410 [cs], Oct. 2017.