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• CARLA Simulator 0.9.14
• conda env create -f src/environment.yml

Note that you may need to set up your Python path to point to CARLA:

export CARLA_ROOT=<PATH-TO-CARLA>
export PYTHONPATH=$PYTHONPATH:$CARLA_ROOT/PythonAPI/carla
export PYTHONPATH=$PYTHONPATH:$CARLA_ROOT/PythonAPI/carla/agents
export PYTHONPATH=$PYTHONPATH:$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.14-py3.7-linux-x86_64.egg

To run the data collector:

1. Run CarlaUE4.sh in your CARLA installation path
2. python data_collector.py --dataset_path <DATASET_PATH> --episode_file <TRAIN_EPISODE_FILE> --n_episodes <NUMBER OF EPISODES>

Example usage: python data_collector.py --dataset_path ./data --episode_file test_suites/Town01_All.txt --n_episodes 10 will randomly sample 10 episodes from Town01_All.txt and save the hdf5 files in ./data.

Follow the instructions in ModifiedDeepestLSTMTinyPilotNet/train.ipynb. TODO: put training code in a script

1. Run CarlaUE4.sh -renderOffScreen in your CARLA installation path
2. Run python evaluate_model.py --episode_file <TEST_EPISODE_FILE> --model <MODEL_FILE> --n_episodes <NUMBER OF EPISODES>

A pygame window should pop up and testing automatically starts. At the end of testing, the following metrics will be reported

• Success rate: $\frac{\text{number of successful episodes}}{\text{total number of episodes}}$
• Success rate weighted by track length: $\frac{\sum S_i l_i}{\sum l_i}$ where $S_i = 0$ if the agent fails to arrive at the target location in episode $i$ and $S_i = 1$ otherwise, and $l_i$ is the distance traveled by the agent.
• Average distance traveled before collision: the average distance traveled by the agent before a collision occurs, calculated for failed cases only

Example usage: python evaluate_model.py --episode_file test_suites/Town02_All.txt --model "ModifiedDeepestLSTMTinyPilotNet/models/v10.0.pth" --n_episodes 100 --combined_control will test the v10.0 model in Town02 for 100 randomly sampled episodes from Town02_All.txt.