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You agree to:

Use the dataset in compliance with the CC BY-NC 4.0 license (non-commercial use only)
Use the dataset for research, development, and safety-related purposes
NOT use the dataset for military, surveillance, or related applications
NOT use the dataset in ways that could cause harm to individuals or society
Respect all applicable laws and regulations
Cite both CARD and CALM (see Citation below) in any resulting work

CALM-Italy

CALM-Italy holds 45 speed bump crossings recorded on Italian roads in the province of Lecce, cut from the CARD recordings. Each crossing is a short stretch of a drive around one speed bump, with the camera frames, depth points, point cloud and raw sensor files of that stretch, the wheel signal that the road height is built from, and, for the qualified crossings, the speed at which two planners drove over it in simulation. The data is meant for training and evaluating speed planning over speed bumps.

A crossing starts 5 s before the bump and ends 5 s after it. 36 of the 45 crossings are the full 10 s long; the others are cut short, down to 3.6 s, where the recording starts or ends close to the bump.

Crossings

Area	Crossings	Area	Crossings
Carmiano	15	Nardò	11
Salice Salentino	12	Monteroni di Lecce	7

The bump in each crossing was measured from the road profile. In the 41 qualified crossings, where this measurement passes the quality check, the bumps are 3.09 to 8.3 cm high and 1.05 to 6.55 m wide. The other 4 are marked low_confidence in CALM-Scenarios, because no clear bump was found near the position the camera flagged. The two figures show every crossing with the measured values, before CALM-Scenarios limits them to the range its road model accepts. The second shows the height divided by the width, which the figures call severity.

manifest.json lists every crossing with the CARD sequence it was cut from, its number of bumps and its duration.

Structure

manifest.json                   list of all crossings
stats/                          the figures in this README
sequences/<area>/<crossing>/
├── img/cam_0/, img/cam_1/      camera frames
├── agg_depth/cam_1/            aggregated depth points in the camera frame
├── labels/                     bump box per cam_1 frame
├── raw/
│   ├── gnss.ubx                raw GNSS log
│   └── data.pcap               raw sensor packet capture
├── export/
│   ├── output.laz              point cloud
│   ├── output.laz.index.json   point cloud scan index
│   ├── wheel_excitement.json   distance driven and contact points of each wheel
│   ├── output.laz.trajectory.json      pose of the car
│   └── output.laz.sensor_export.json   GPS and IMU samples
├── comfort_profiles/           only for the 41 qualified crossings
│   ├── ppo.json
│   └── mpc.json
└── meta.yaml                   window and bump position in the source sequence

Four crossings, nardo_sb01, nardo_sb11, salice_salentino_sb04 and salice_salentino_sb12, have no data.pcap. The LiDAR capture of their source recording ends before the window starts, so no packets exist for it, and their meta.yaml lists pcap: unavailable.

For two crossings, carmiano_sb03 and carmiano_sb04, the label folder holds more files than the image folders: 66 and 101 label files for 54 and 89 camera frames, while manifest.json and meta.yaml count 54 and 89 labels.

File formats

Labels. CARD's labels exist for cam_1 only. Each file in labels/ belongs to the cam_1 frame with the same timestamp and has one line per box in the YOLO format: class id, then the box centre x and y, width and height as fractions of the image size, for example 1 0.484491 0.525675 0.223061 0.011496. Class 1 is a speed bump; 32 lines in this dataset have class 0, which CALM does not use. An empty file means that the frame shows no bump.

Depth. Each .npz file in agg_depth/cam_1/ holds aggregated depth points for one frame: a single array pts_cam of shape N x 3 (float32) with x, y and z of each point in the camera frame. One file, salice_salentino/salice_salentino_sb06/agg_depth/cam_1/cam_1_122301894.npz, is empty (0 bytes) and cannot be loaded; the source copy is empty as well.

Wheel signal. CARD's export/wheel_excitement.json has one entry per wheel under wheel_results (cariad_wheel_FL, _FR, _RL and _RR). Its wheel_excitement list has one row per centimetre driven, with 4 numbers: the timestamp in microseconds, a second timestamp in microseconds on another clock (a constant offset from the first), the distance driven in metres, and the vertical excitation of the wheel in metres. contact_point_trajectory.poses gives, at the same timestamps, the pose of the point where the wheel touches the road as [timestamp, [x, y, z, qw, qx, qy, qz]]. CALM builds the road height in CALM-Scenarios from the contact point heights of the two front wheels, placed along the road by the distance column, and replays the recorded speed from the time and distance of the front left wheel.

meta.yaml. Timestamps are in microseconds on the clock of the recording, the same clock as in the frame file names. Distances are in metres.

Key	Meaning
`source_sequence`	The CARD recording the crossing was cut from
`source_window_index`	Number of the bump encounter within that recording
`window_index`	Number of the crossing within its area, as in the folder name
`sb_count`	Number of bumps in the crossing
`encounter`	The `cam_1` frames in which the labels show the bump: `start_ts`, `end_ts` and `n_frames`
`anchor_ts`	Time of the bump, the end of the encounter
`window.start_ts`, `window.end_ts`	Start and end of the crossing
`clipped_pre`, `clipped_post`	True when the window starts later or ends earlier than planned, for example because the recording starts or ends there
`s_bump`	Distance driven from the start of the recording to the bump
`s_span`	Distance driven within the window
`contents`	Number of items copied for each part: files, poses, rows, scans or samples; `pcap` is `unavailable` when the recording has no packets for the window

Speed profiles

comfort_profiles/ holds two simulated drives over the crossing. ppo.json comes from the trained PPO policy of CALM and mpc.json from a model predictive controller that plans with the same objective. Each file gives the travel time and a list of samples, each with the time t_s in seconds, the distance along the road s_m in metres and the speed v_kmh in km/h.

What CALM added

Most of what a crossing holds comes from CARD-Italy, cut to the window: the camera frames, the aggregated depth points, the bump labels, the point cloud, the trajectory of the car, the wheel signal in export/wheel_excitement.json, and the raw GNSS log and sensor packets.

CARD recorded long drives. CALM cut each crossing out of them, 5 s before and 5 s after the bump. Where a recording starts or ends close to the bump, the window is shorter.

Besides the cut, CALM added three things:

Addition	What it is	What it is for
`meta.yaml`	Where the window lies in the source recording and where the bump is	Finding the crossing again in the CARD sequence
`manifest.json`	The list of all crossings with the CARD sequence, number of bumps and duration of each	Finding a crossing without opening every folder
`comfort_profiles/`	Simulated speed over the crossing from the PPO policy and the MPC planner, for the qualified crossings only	Showing how a planner slows down before the bump and speeds up again

Use

The compact scenarios derived from these crossings, with the road height, the bump and the camera detections, are in CALM-Scenarios, together with the training and evaluation split. The code is in the CALM repository. calm reproduce trains and evaluates the policy on the scenarios. Set CALM_SEQUENCES to the sequences folder of this dataset to compare the result with the recorded drives.

License

CC BY-NC 4.0. Non-commercial use only. The material copied unchanged from CARD-Italy keeps its CC BY-NC 4.0 terms. The additions by CALM (meta.yaml, manifest.json and the speed profiles) are released under the same license.

Citation

Please cite CARD, which recorded the drives, and CALM.

@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},
  eprint    = {2605.05014},
  archivePrefix = {arXiv}
}

@misc{calm_dataset,
  title  = {CALM: Comfort-Aware Longitudinal Motion Planning},
  author = {Mohammed El Said Azab Abdelazim},
  year   = {2026},
  url    = {https://huggingface.co/CALM-Dataset}
}

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Paper for CALM-Dataset/CALM-Italy

CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography

Paper • 2605.05014 • Published May 7