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CALM – Germany
Comfort-labeled speed bump crossings from German roads: Ingolstadt, Augsburg, Taufkirchen, Munich, and Unterföhring. Each crossing comes with camera frames, sensor data, trajectory, and a recommended crossing speed for ride comfort, meant for training and evaluating speed planning around road bumps.
The compact per-crossing scenario data (bump position/height/width, speed profile) and the camera-detection model live in CALM-Scenarios.
Structure
<crossing>/
├── img/
│ ├── cam_0/ camera frames
│ └── cam_1/
├── agg_depth/
│ └── cam_1/ aggregated depth maps
├── labels/ per-frame bump detection labels
├── raw/
│ ├── gnss.ubx raw GNSS log
│ └── data.pcap raw sensor packet capture
├── export/
│ ├── output.laz raw point cloud
│ ├── output.laz.index.json point cloud scan index
│ ├── wheel_excitement.json wheel-height signal
│ ├── output.laz.trajectory.json car pose/trajectory
│ └── output.laz.sensor_export.json other sliced sensor channels
├── comfort_profiles/
│ ├── ppo.json recommended crossing speed (PPO agent)
│ └── mpc.json recommended crossing speed (MPC baseline)
└── meta.yaml
License
CC BY 4.0, same as CARD-Germany. Attribution required, commercial use permitted.
Citation
Please cite both CARD (the source sequences) and CALM (this extension).
@inproceedings{elazab2025card,
title = {CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography},
author = {Elazab, Gasser and Neuhaus, Frank and Ko{\ss}, Tilman and Splietker, Malte and Date, Aditya and Unterreiner, Michael and Jansen, Maximilian and Hellwich, Olaf},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2026}
}
@misc{calm_dataset,
title = {CALM: Comfort-Aware Longitudinal Motion Planning},
author = {Mohammed S. Azab Abdelazim},
year = {2026},
url = {https://huggingface.co/CALM-Dataset}
}
Credit
Built on the CARD dataset by Gasser Elazab and the CARIAD team.
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