#### NOTE This is a **community plugin**, an external project maintained by its respective author. Community plugins are not part of FiftyOne core and may change independently. Please review each plugin’s documentation and license before use. ![GitHub Repo](https://img.shields.io/badge/GitHub-Repository-black?logo=github) # BDDOIA Safe/Unsafe Action Dataset for FiftyOne This dataset is designed for benchmarking safety-aware vision-language models within the FiftyOne ecosystem, particularly for autonomous driving safety evaluation. ![bddi-ezgif com-video-to-webp-converter](https://github.com/user-attachments/assets/fea52cd0-3e9d-4d16-804a-ab91c3dd8db3) ## Dataset Overview ### Source & Citation - **Source**: Based on the BDD-OIA dataset introduced in [Xu et al., CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Explainable_Object-Induced_Action_Decision_for_Autonomous_Vehicles_CVPR_2020_paper.pdf) - **Original Paper**: “Explainable Object-Induced Action Decision for Autonomous Vehicles” - **License**: Creative Commons Attribution 4.0 International ### Dataset Statistics | Split | Images | Annotations | Safe Actions | Unsafe Actions | |----------------|----------|---------------|------------------------------------------------------------------------------|------------------------------------------------------------------------------| | **Train** | 16,082 | 16,082 | forward: 8,770, stop: 6,887, left: 218, right: 163, confuse: 16, unknown: 28 | stop: 8,770, forward: 6,887, right: 218, left: 163, confuse: 16, unknown: 28 | | **Validation** | 2,270 | 2,270 | stop: 958, forward: 1,237, left: 39, right: 24, confuse: 6, unknown: 6 | forward: 958, stop: 1,237, left: 24, right: 39, confuse: 6, unknown: 6 | | **Test** | 4,572 | 4,572 | forward: 2,484, stop: 1,965, left: 57, right: 51, confuse: 8, unknown: 7 | stop: 2,484, forward: 1,965, right: 57, left: 51, confuse: 8, unknown: 7 | **Total**: 22,924 images across all splits ### Action Categories Each image has one **safe action** and one corresponding **unsafe action**: | Index | Action | Description | |---------|-----------|------------------------------| | 0 | `forward` | Continue driving forward | | 1 | `stop` | Stop the vehicle | | 2 | `left` | Turn left | | 3 | `right` | Turn right | | 4 | `confuse` | Confused/uncertain situation | ### Safety Mapping The dataset creates unsafe scenarios by flipping actions: - `forward` ↔ `stop` (0↔1) - `left` ↔ `right` (2↔3) - `confuse` stays the same (4→4) ### Explanation Classes (Reasons) Each annotation includes a multi-hot vector of 21 reasoning categories: | Index | Explanation Class | Train Count | Val Count | Test Count | |---------|------------------------------|---------------|-------------|--------------| | 0 | Traffic light is green | 5,474 | 752 | 1,574 | | 1 | Follow traffic | 2,448 | 372 | 667 | | 2 | Road is clear | 3,422 | 474 | 941 | | 3 | Traffic light | 3,760 | 530 | 1,083 | | 4 | Traffic sign | 1,095 | 141 | 302 | | 5 | Obstacle: car | 170 | 22 | 39 | | 6 | Obstacle: person | 116 | 13 | 34 | | 7 | Obstacle: rider | 3,699 | 512 | 1,037 | | 8 | Obstacle: others | 328 | 36 | 89 | | 9 | No lane on the left | 100 | 21 | 29 | | 10 | Obstacles on the left lane | 464 | 66 | 136 | | 11 | Solid line on the left | 220 | 28 | 68 | | 12 | On the left-turn lane | 115 | 16 | 23 | | 13 | Traffic light allows (left) | 629 | 81 | 175 | | 14 | Front car turning left | 270 | 26 | 69 | | 15 | No lane on the right | 3,173 | 456 | 860 | | 16 | Obstacles on the right lane | 3,171 | 438 | 897 | | 17 | Solid line on the right | 2,582 | 359 | 713 | | 18 | On the right-turn lane | 4,293 | 595 | 1,182 | | 19 | Traffic light allows (right) | 2,791 | 396 | 826 | | 20 | Front car turning right | 1,558 | 194 | 403 | ## Quick Start ### Installation ```bash pip install fiftyone ``` ### Load Dataset via FiftyOne Zoo ```python import fiftyone as fo import fiftyone.zoo as foz # Load the dataset (automatically downloads if not cached) dataset = foz.load_zoo_dataset( "https://github.com/AdonaiVera/bddoia-fiftyone", split="validation", # or "train", "test" max_samples=10000 # limit for quick exploration ) # Launch the FiftyOne App session = fo.launch_app(dataset) ``` ### Load Specific Split ```python # Load training data train_dataset = foz.load_zoo_dataset("https://github.com/AdonaiVera/bddoia-fiftyone", split="train") # Load test data test_dataset = foz.load_zoo_dataset("https://github.com/AdonaiVera/bddoia-fiftyone", split="test") # Load all splits all_splits = foz.load_zoo_dataset("https://github.com/AdonaiVera/bddoia-fiftyone") ``` ## Dataset Exploration ### View Dataset Statistics ```python # Get dataset info print(dataset) # View sample annotations sample = dataset.first() print("Safe action:", sample.ground_truth.label) print("Unsafe action:", sample.unsafe_action.label) print("Reasons:", sample.reasons.labels) ``` ### Filter by Actions ```python # View all forward actions forward_samples = dataset.match(F("ground_truth.label") == "forward") print(f"Found {len(forward_samples)} forward actions") # View all stop actions stop_samples = dataset.match(F("ground_truth.label") == "stop") print(f"Found {len(stop_samples)} stop actions") ``` ### Filter by Reasons ```python # View samples with traffic light issues traffic_light_samples = dataset.match( F("reasons.labels").contains("Traffic light is green") ) print(f"Found {len(traffic_light_samples)} samples with green traffic lights") ``` ## Analysis & Visualization ### Action Distribution ```python # Plot action distributions import fiftyone.core.plots as fop # Safe actions safe_plot = dataset.count("ground_truth.label") safe_plot.show() # Unsafe actions unsafe_plot = dataset.count("unsafe_action.label") unsafe_plot.show() ``` ### Reason Analysis ```python # Most common reasons reason_counts = dataset.count("reasons.labels") print("Top reasons:", reason_counts[:10]) # Co-occurrence analysis cooccurrence = dataset.count("reasons.labels", "ground_truth.label") print("Action-Reason co-occurrence:", cooccurrence) ``` ### Custom Filters ```python # Create custom view custom_view = dataset.match( (F("ground_truth.label") == "forward") & (F("reasons.labels").contains("Traffic light is green")) ) print(f"Found {len(custom_view)} samples") ``` ## References - **Original Paper**: Xu *et al.*, “Explainable Object-Induced Action Decision for Autonomous Vehicles”, CVPR 2020 - **Dataset**: [BDD-OIA Project](https://twizwei.github.io/bddoia_project/) - **FiftyOne**: [Voxel51 Documentation](https://docs.voxel51.com/) - **Remote Zoo**: [FiftyOne Remote Datasets](https://docs.voxel51.com/dataset_zoo/remote.html) ## Contributing This dataset is designed for the workshop on “Benchmarking Safe Driving Perception with FiftyOne”. For questions or contributions: 1. Check the [FiftyOne documentation](https://docs.voxel51.com/) 2. Review the [remote dataset guide](https://docs.voxel51.com/dataset_zoo/remote.html) 3. Explore the [FiftyOne community](https://community.voxel51.com/) ## License This dataset extension is released under the Creative Commons Attribution 4.0 International License, following the original BDD-OIA dataset license. --- **Note**: This dataset is specifically designed for safety evaluation in autonomous driving scenarios. Always ensure proper validation and testing when using these annotations for real-world applications.