[TOC]

Python Scripts for the INTERACTION dataset and the INTERPRET Competition

• these scripts assist you to visualize the INTERACTION dataset and to process the INTERACTION Dataset into segments that can be directly used for training, validation and test in the INTERPRET challenge.
• for details about and access to the dataset, visit https://interaction-dataset.com/
• for details about the INTERPRET challenge, visit http://challenge.interaction-dataset.com/prediction-challenge/intro
• If you want to visualize the INTERACTION dataset, please refer to the github at https://github.com/interaction-dataset/interaction-dataset
• python3 is supported

Required Python Packages

• csv: for reading the csv track files
• to work with the map:
  • either lanelet2 for most convenient map usage
    • see https://github.com/fzi-forschungszentrum-informatik/Lanelet2 for details
  • or
    • pyproj
    • xml
• numpy, os, sys, functools, shutil andargparse for processing the data
• math, numpy,time and matplotlib for visualizing the scenarios
• use test_imports.py to test whether you have all necessary packages installed

Dataset Visualization

Usage

• copy/download the INTERACTION drone data into the right place
  • copy/download the track files into the folder recorded_trackfiles, keep one folder per scenario, as in your download
  • copy/download the maps into the folder maps
  • your folder structure should look like in folder-structure.md
• to visualize the data
  • run ./main_visualize_data.py <scenario_name> <trackfile_number (default=0)> from folder python to visualize scenarios
• if you only want to load and work with the track files
  • run ./main_load_track_file.py <tracks_filename> from folder python to load tracks

Test Usage without Dataset

• to test the visualization
  • run ./main_visualize_data.py .TestScenarioForScripts
• to test loading the data
  • run ./main_load_track_file.py ../recorded_trackfiles/.TestScenarioForScripts/vehicle_tracks_000.csv

Prepare Training Dataset for the INTERPRET Challenge

• copy/download the INTERACTION drone data into the right place
  • copy/download the track files into the folder recorded_trackfiles, keep one folder per scenario, as in your download
  • copy/download the maps into the folder maps
  • copy/download the suggested split .txt file ( validation-set-list_INTERACTION-dataset_v1.txt ) for the train and validation set into the folder recorded_trackfiles
  • your folder structure should look like in folder-structure.md
• to split all track files into train and validation subfolders
  • run ./split_train_val_script.py <path_to_the_suggested_split_txt_file> <path_to_the_recorded_trackfiles> from folder python to split the trackfiles in the corresponding scenarios into two subfolders train and val . (Internal note: the original csv files in the folder will be moved into the two newly created subfolders)
• to prepare the data into segments that can be directly utilized for the INTERPRET prediction
  • run ./segment_data.py <options> <path_to_folder_or_file_from_scenario_folder> number_of_frames_in_segments gap_frames_between_segments from folder python
    • If users choose option=default, all trackfiles in both the train and val subfolders in all scenarios will be segmented. The segmented csv files will be saved in newly created subfolders segmented in corresponding train and validation folders. In the meanwhile, a folder sorted will also be generated in the train and val subfolders which contains the csv files that are sorted based on the frame_id instead of the track_id in the file.
    • If users choose option=dir , and specify the <path_to_folder_or_file_from_scenario_folder> as a folder, then all csv files under this folder will be segmented, saved in a newly created subfolder segmented. If users choose option=file , and specify <path_to_folder_or_file_from_scenario_folder> as a .csv file, then the csv file will be segmented, saved in the subfolder segmented.
    • All the segmented track files will follow the names of the original csv files.
• to split all track files and prepare all data into segments
  • from the python folder, run ./split_train_val_script.py <path_to_the_suggested_split_txt_file> <path_to_the_recorded_trackfiles>
  • from the python folder, run ./segment_data.py default <path_to_recorded_track_files> number_of_frames_in_segments gap_frames_between_segments to split all the train and validation sets for all scenarios and segment all track files. The first parameter number_of_frames_in_segments determines how many frames (including both historical and predicted frames) are contained in each training case, and users can decide the length of historical frames and predicted frames as they need. The second gap_frames_between_segments defines the gap between two successive test cases with a same agent. Typically, you should set gap_frames_between_segments > number_of_frames_in_segments to avoid overlaps between different test cases. Moreover, in the segmented files, there is one extra column compared to the original track files, i.e., the agent_role column. If agent_role="agent", then that agent will be the one whose future trajectories need to be predicted, and all surronding vehicles will be marked as others.

Note: The segmentation process will take a while, so please be patient.