📦 A comprehensive data collection and labeling toolkit for CARLA Simulator
Data collection and labeling tools for CARLA Simulator. This toolkit provides an efficient pipeline for generating high-quality autonomous driving datasets with support for multiple sensor types and standard dataset formats.
🌟 This work is part of the CarlaFLCAV project. Feel free to give us a star!
- ✅ Multi-Sensor Support: RGB Camera, Semantic Segmentation, LiDAR, Semantic LiDAR, Radar, IMU, GNSS
- ✅ V2X Communication: Vehicle-to-Everything (V2X) sensor support for V2V and V2I scenarios
- V2X CAM sensors (ETSI standard) for automatic message generation
- V2X Custom sensors for manual message broadcasting
- Realistic wireless channel modeling with path loss simulation
- ✅ Infrastructure Support: Roadside unit (RSU) simulation for V2X scenarios
- Static infrastructure actors with sensor attachment
- One-way message broadcasting from infrastructure to vehicles
- Support for V2I (Vehicle-to-Infrastructure) communication
- ✅ Multiple Dataset Formats: KITTI Object, YOLOv5, Argoverse
- ✅ Flexible Configuration: YAML-based configuration with validation
- ✅ Route Editor: Interactive tool for creating custom vehicle routes with topology-aware path planning
- ✅ BEV Map Capture: Tool for generating bird's-eye view maps of entire CARLA maps
- ✅ Synchronized Recording: Synchronized multi-vehicle and multi-sensor data collection
- ✅ Visualization Tools: Built-in point cloud and data visualization utilities
- ✅ Autopilot Integration: Automatic vehicle control using CARLA's traffic manager
# 1. Clone and install
git clone https://github.com/KevinLADLee/carla_dataset_tools.git
cd carla_dataset_tools
pip3 install -r requirements.txt
# 2. Set environment variable
export CARLA_ROOT=/path/to/your/carla
# 3. Start CARLA
cd $CARLA_ROOT && ./CarlaUE4.sh
# 4. Record data (in a new terminal)
cd carla_dataset_tools
python3 data_recorder.py --profile kitti
# 5. Generate labels
python3 label_tools/kitti_objects_label.py -r record_YYYY_MMDD_HHMM -v vehicle_1st
# 6. Visulize output dataset
python3 tools/viz_lidar.py --type kitti --source dataset/record_YYYY_MMDD_HHMM/vehicle_1st/kitti_object/training/velodyne
User Guide - Complete installation and usage guide
- Installation and prerequisites
- Recording data with different profiles
- Route Editor - Create custom vehicle routes interactively
- Generating labels (KITTI, YOLOv5, Argoverse)
- Data visualization
- Troubleshooting
中文用户指南 - 完整的安装和使用指南
Developer Guide - Technical documentation for developers
- Architecture overview
- Configuration system details (maps, weather, sensors)
- API reference
- Extending the toolkit
- Development workflow
中文开发者指南 - 开发者技术文档
Pre-configured profiles for different dataset styles:
default- General purpose with multiple vehicles and sensorskitti- KITTI-style (Velodyne HDL-64E, standard cameras)argoverse- Argoverse-style with ring camerassimple- Minimal configuration for testingroute_example- Demonstrates vehicle route following featurev2x_test- V2X communication testing (V2V and V2I scenarios)bev- Bird's-eye view camera configuration for map capture
# List available profiles
python3 tools/config_list.py
# Use a profile
python3 data_recorder.py --profile kitti
# Create custom vehicle routes
python3 tools/editor_route.py --map Town02 --name my_route
# Capture BEV map of entire CARLA map
python3 tools/capture_map_bev.py --map Town02 --output ./bev_output
# Validate a configuration
python3 tools/config_validate.py --profile kittiSee Developer Guide for complete configuration reference.
- KITTI Object Detection - 3D bounding boxes with calibration
- YOLOv5 - 2D bounding box annotations
- Argoverse - Ring camera setup (experimental)
- CARLA Simulator >= 0.9.16
- Python >= 3.8
- Operating System: Linux (recommended) / Windows
Download CARLA: https://github.com/carla-simulator/carla/releases
carla_dataset_tools/
├── config/ # Configuration management
│ ├── config_manager.py # YAML config loader and validator
│ └── profiles/ # Pre-configured profiles (default, kitti, argoverse, simple, route_example)
├── docs/ # Documentation
│ ├── USER_GUIDE.md # User guide (English)
│ ├── USER_GUIDE_CN.md # User guide (Chinese)
│ ├── DEVELOPER.md # Developer guide (English)
│ └── DEVELOPER_CN.md # Developer guide (Chinese)
├── label_tools/ # Labeling scripts (KITTI, YOLO, Argoverse)
├── recorder/ # Core recording modules
├── routes/ # Vehicle route definitions (YAML + PKL)
├── core/ # Core shared modules (geometry, transform, logger)
├── tools/ # CLI utility scripts
│ ├── editor_route.py # Route editor
│ ├── capture_map_bev.py # BEV map capture tool
│ ├── viz_lidar.py # LiDAR visualization
│ └── ... # Other utilities
└── data_recorder.py # Main recording script
Contributions are welcome! Areas for contribution:
- Additional dataset format support (nuScenes, Waymo, etc.)
- Enhanced documentation and examples
- Bug fixes and performance improvements
- New sensor types or features
Please submit pull requests to the main repository.
If you use this tool in your research, please cite:
@article{wang2022federated,
title={Federated deep learning meets autonomous vehicle perception: Design and verification},
author={Wang, Shuai and Li, Chengyang and Ng, Derrick Wing Kwan and Eldar, Yonina C and Poor, H Vincent and Hao, Qi and Xu, Chengzhong},
journal={IEEE network},
volume={37},
number={3},
pages={16--25},
year={2022},
publisher={IEEE}
}This project builds upon:
- CARLA Simulator - Open-source autonomous driving simulator
- CARLA ROS Bridge - ROS integration for CARLA
- CARLA_INVS - Infrastructure and vehicle simulation
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
- Issues: GitHub Issues
- Project: CarlaFLCAV
- Documentation: User Guide | Developer Guide
⭐ If this project helps your research, please give us a star! ⭐