Synchronized multimodal driving data was collected in the CARLA simulator using the autopilot feature. The dataset includes RGB images, semantic segmentation, LiDAR point clouds, 2D bounding boxes, and ego-vehicle state data across varied weather and traffic conditions. It was created by immanuelpeter and last updated on 2025-11-29.
Use Cases
- Training sensor fusion models based on synchronized RGB, LiDAR, and semantic segmentation data.
- Developing and evaluating imitation learning agents based on ego-vehicle state and control signals.
- Benchmarking autonomous driving systems based on data collected across varied weather and traffic densities.
- Researching 2D and 3D object detection based on provided bounding boxes and point clouds.
Strengths
- Data is synchronized across multiple modalities including RGB, LiDAR, and semantic segmentation.
- Collection covers varied environmental conditions such as weather and traffic densities.
- Includes multiple data types relevant for autonomous driving research, such as point clouds and control signals.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count, file formats, and license are unknown, which may limit suitability assessment.
- Data is simulated, which may not fully reflect real-world driving conditions.
Provenance
- Source
- huggingface
- Collection Method
- Collected in the CARLA simulator using the autopilot feature.
- Time Range
- null
- Freshness
- Last updated 2025-11-29 01:32:01; freshness should be verified.
- Geography
- null