This is a part of the bigger project to bring the more realistic pedestrian movements to CARLA. It isn't intended for fully standalone use. Please see the main project README.md or Adversarial Cases for Autonomous Vehicles (ARCANE) project website for details.
Copy the .env.template file to .env and edit the values as required.
Run python -m pedestrians_scenarios --help to see the full list of available options.
This command is the entrypoint for dataset-generation related tasks.
python -m pedestrians_scenarios datasets --helpThis command generates the dataset according to specified parameters.
python -m pedestrians_scenarios datasets generate <options>-h,--helpshow help message and exit. Please use this for full list of available options.-c CONFIG,--config CONFIGConfig file path. Settings in this file will override those passed via CLI
--seed SEEDSeed used by the everything (default: 22752). If you want to generate a dataset with the same options, but in multiple shots, you need to manually change the seed with each run. You should ensure the seed differs by at leastnumber_of_clips * failure_multiplierbetween runs.--fps FPSFPS of the simulation (default: 30)
--outputs_dir OUTPUTS_DIRDirectory to store outputs (default: ./datasets).--number_of_clips NUMBER_OF_CLIPSTotal number of clips to generate (default: 512).--clip_length_in_frames CLIP_LENGTH_IN_FRAMESLength of each clip in frames (default: 900).--batch_size BATCH_SIZENumber of clips in each batch (default: 1). This is the number of scenarios that will be simulated in the same world at the same time. This is useful for speeding up the generation process, but it also means that the generated clips can contain pedestrians from different scenarios in the camera viewport.--camera_fov CAMERA_FOVCamera horizontal FOV in degrees (default: 90.0).--camera_image_size CAMERA_IMAGE_SIZECamera image size in pixels as a (width, height) tuple (default: (1600,600)).--waypoint_jitter_scale WAYPOINT_JITTER_SCALEScale of jitter applied to waypoints (default: 1.0). This determines how much the pedestrians will deviate from the "ideal" path. The higher the value, the more they will deviate.--failure_multiplier FAILURE_MULTIPLIERMultiplier for number of clips to generate in case of failure (default: 2). As generating a clip is a stochastic process, it can fail (CARLA server shuts down, pedestrian is stuck, something obstructs the view of the pedestrian during the whole clip etc.). This option determines how many tries in total the process will make before giving up. The total number of tries isnumber_of_clips * failure_multiplier.--overwriteOverwrite existing output dir (default: False). As a default, if generator detects that the output directory already exists, it will abort the process. This option forces the generator to overwrite the existing directory. Please note that this will override the files in the directory, possibly resulting in data loss.
Example command can look like this:
```sh
python -m pedestrians_scenarios datasets generate \
--outputs_dir /outputs/scenarios/small-sample \
--number_of_clips 16 \
--failure_multiplier 8
Allows to merge multiple datasets into one, ensuring that the clip IDs are unique.
python -m pedestrians_scenarios datasets merge --input_dirs <list_of_input_dirs> --output_dir <output_dir>Especially in the older versions of the generator, there were many output videos which did not contain pedestrians. Therefore, we developed an automated cleaning procedure that checks if pedestrian is visible at any point in the video using YOLOv3. A usage example:
python -m pedestrians_scenarios datasets clean --dataset_dir /outputs/dataset-dirThe command above will run in a dry mode (the files will not be removed). If you actually want to remove them run the command with --remove flag:
python -m pedestrians_scenarios datasets clean --dataset_dir /outputs/dataset-dir --removeKeep in mind that the video files which do not contain pedestrians in the folders you provide as the arguments will be removed from disk. The csv file will also be changed by removing rows which refer to videos which do not contain pedestrians.
Note: The cleaning procedure is not perfect. It may remove some videos which contain pedestrians or leave some videos which do not contain pedestrians. It is also quite slow. Therefore, it is better to not disable semantic cameras during dataset generation and take advantage of the (now) build-in filtering.
To launch the basic ScenarioRunner, run the following (example) command:
cd /app/third_party/scenario_runner/
python scenario_runner.py --host server --scenario FollowLeadingVehicle_1 --reloadWorld --output
# OR
python scenario_runner.py --host server --route ./srunner/data/routes_debug.xml ./srunner/data/all_towns_traffic_scenarios.json 0 --agent srunner/autoagents/npc_agent.py --output --reloadWorldThe really important part is the --host parameter. We pass it the name of the carla server container
as specified in our docker-compose.yml file.
Since all of the code is running inside a docker container, to see that is happening we would need to
either setup some X11 forwarding magic (no, thank you) or approach it in a different way (yes, please).
To see what's happening we're going to use the carlaviz tool, which lets to preview the ego vehicle in the browser.
Now, there are basically three configurations to choose from:
- You are running everything on your own local machine. In this case, you need to ensure the
carlavizports are bound to correct ports on your machine, by editing thecarlaviz/.envfile:
# this can actually be any port, you will use it in the browser, e.g. http://localhost:8080
CARLAVIZ_FRONTEND_MAPPED_PORT=8080
# this needs to be exactly $CARLAVIZ_BACKEND_PORT (8081 by default), as it is used by the carlaviz frontend
CARLAVIZ_BACKEND_MAPPED_PORT=8081- You are running everything on a remote server, but you can publish the carlaviz backend port. In this case, you need to ensure the
carlavizports are bound to correct ports on the remote server, by editing thecarlaviz/.envfile:
CARLAVIZ_BACKEND_HOST=your_server_ip_or_hostnameIf you change ports from default 8081, please ensure CARLAVIZ_BACKEND_MAPPED_PORT and CARLAVIZ_BACKEND_PORT match.
- You are running everything on a remote server and you need to go through the access node. In this case, you need to ensure the
carlavizports are bound to 'intermediate' ports on the remote server, and that you have the forwarding set up and running. First, editing thecarlaviz/.envfile to set the intermediate port numbers:
# numbers can be whatever you like, just ensure they match with forwarding config
CARLAVIZ_FRONTEND_MAPPED_PORT=49164
CARLAVIZ_BACKEND_MAPPED_PORT=49165Then, you need to set up the forwarding on your local machine. In your .shh/config file, add something like this:
Host forward_carlaviz
HostName remote_server_domain_or_ip_as_visible_from_access_node
LocalForward 8080 127.0.0.1:49164
LocalForward 8081 127.0.0.1:49165
User remote_server_username
ProxyJump access_node_username@access_node_domain_or_ip # skip this if there is no access node
ServerAliveInterval 30
ServerAliveCountMax 3And finally, you need to run the following command on your local machine once the carla-pedestrians_viz_1 container is running (-v is optional):
ssh -v -N forward_carlavizImportant note: if you need to restart the carlaviz container, kill the forwarding first, then restart carlaviz and wait for it to be running, and only then start forwarding again. Otherwise you will get stuck with "Launch the backend and refresh" message in the browser.
If you use this repo please cite:
@misc{wielgosz2023carlabsp,
title={{CARLA-BSP}: a simulated dataset with pedestrians},
author={Maciej Wielgosz and Antonio M. López and Muhammad Naveed Riaz},
month={May},
year={2023},
eprint={2305.00204},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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This project has been set up using PyScaffold 4.1.1. For details and usage information on PyScaffold see https://pyscaffold.org/.