The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: Mismatching child array lengths
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 294, in _recursive_load_arrays
sarr = pa.StructArray.from_arrays(values, names=keys)
File "pyarrow/array.pxi", line 4306, in pyarrow.lib.StructArray.from_arrays
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Mismatching child array lengthsNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ICR-DS: In-Cabin Radar Driver State Dataset
ICR-DS (In-Cabin Radar Driver State) is a multimodal naturalistic driving dataset for driver vigilance and physiological state monitoring. The dataset was collected from 28 participants during approximately 50 minutes of real-world urban and highway driving using synchronised wearable physiological sensing, radar, and an in-cabin RGB camera.
To provide an objective measure of vigilance, each participant completed a Psychomotor Vigilance Test (PVT) before and after each drive. The dataset therefore supports longitudinal analysis of behavioural and physiological driver-state measurements, as well as future development of non-contact physiological monitoring using in-cabin radar sensing.
Overview
Each recording session is stored as a single HDF5 file. The file contains:
- raw signals retained at native resolution
- processed metrics computed from windowed aggregation
- session-level attributes describing the participant, timestamps, sensor configuration, and modality availability
- PVT summaries collected before and after the drive
The dataset includes synchronised recordings from:
- a 60 GHz FMCW radar sensor
- a BioHarness 3 chest strap
- an in-cabin RGB camera
- session-level PVT outcomes
OpenFace was applied to the video stream to derive eye and head-behaviour measurements.
Session Attributes
Each HDF5 file contains the following session attributes:
| Attribute | Example | Description |
|---|---|---|
| session_id | "s027_2026-05-08" | Session identifier |
| subject_id | "s027" | Subject identifier |
| start_time_iso | "2026-05-08T11:27:21Z" | Session start time in UTC ISO-8601 format |
| end_time_iso | "2026-05-08T11:28:32Z" | Session end time in UTC ISO-8601 format |
| frame_period_s | 0.09 | Radar frame period in seconds |
| carrier_freq_hz | 60000000000 | Radar carrier frequency |
| wavelength_m | 0.005 | Radar wavelength |
| range_resolution_m | 0.0843 | Radar range resolution |
| has_radar | "present" | Radar availability flag |
| has_vision | "present" | Vision availability flag |
| has_bioharness | "present" | BioHarness availability flag |
| has_gps | "present" | GPS availability flag |
| has_pvt | "present" | PVT availability flag |
HDF5 File Structure
Each session file follows this structure:
/
βββ raw/
β βββ radar/
β β βββ beamformed_cube
β βββ physiology/
β β βββ hr_1hz
β β βββ br_1hz
β β βββ ibi
β β βββ ibi_ts
β βββ vision/
β β βββ landmarks_raw
β β βββ gaze_xy
β β βββ pose_rxyz
β β βββ eye_open
β β βββ confidence
β βββ labels/
β βββ motion_flag
β βββ predrive
β βββ postdrive
βββ processed/
βββ physiology/
β βββ mean_hr
β βββ mean_br
β βββ rmssd
β βββ sdnn
β βββ pnn50
β βββ hf
βββ vision/
βββ blink_rate
βββ perclos
βββ blink_duration
βββ gaze_speed
βββ gaze_x_std
βββ gaze_y_std
βββ head_speed
βββ head_yaw_std
βββ head_pitch_std
βββ head_roll_std
Radar Data
Each radar sample is stored as a beamformed complex IQ tensor with shape:
(Window, IQ, Doppler, Direction, Range) = (200, 2, 5, 63, 7)
Radar tensor dimensions
| Dimension | Size | Description |
|---|---|---|
| Window | 200 | Consecutive radar frames sampled every 90 ms, giving approximately 18 seconds of observation |
| IQ | 2 | Real and imaginary components of the beamformed signal |
| Doppler | 5 | Low-velocity Doppler bins centred on zero Doppler |
| Direction | 63 | Beamformed look directions (9 azimuth Γ 7 elevation) |
| Range | 7 | Selected range bins corresponding to the driver region |
The raw radar stream is retained at native resolution and stored in raw/radar/beamformed_cube.
Physiological Signals
The BioHarness 3 chest strap provided:
- heart rate at 1 Hz
- breathing rate at 1 Hz
- R-R intervals as event-based measurements
Raw physiology is stored in:
- raw/physiology/hr_1hz
- raw/physiology/br_1hz
- raw/physiology/ibi
- raw/physiology/ibi_ts
Vision Signals
The in-cabin RGB camera was mounted next to the radar and focused on the driver's head and face. OpenFace was applied to the video stream to derive eye and head-behaviour measurements.
Raw vision features are stored in:
- raw/vision/landmarks_raw
- raw/vision/gaze_xy
- raw/vision/pose_rxyz
- raw/vision/eye_open
- raw/vision/confidence
PVT Labels
To provide objective vigilance measurements, participants completed a Psychomotor Vigilance Test before and after each drive.
The following outcomes were extracted:
- mean_rt_ms
- median_rt_ms
- reciprocal_rt_1_per_s
- lapse_rate
- commission_rate
These are stored in:
- raw/labels/predrive
- raw/labels/postdrive
Motion Flag
A motion flag is stored in:
- raw/labels/motion_flag
This signal is generated from aligned vehicle and body motion sources and stored as a session-aligned derived label signal.
Temporal Alignment
Each modality had its own time stream and was synchronised using the latest common start time, with the earliest end time defining the shared analysis interval.
Raw signals were retained at their native resolution. Feature extraction begins after a 5-minute offset to enable reliable HRV estimation and maintain alignment across modalities.
Processed Features
Processed features were computed from the raw signals using fixed temporal aggregation windows.
Physiology Metrics
Physiological features were computed every minute using a 5-minute rolling window.
| Metric | Unit | Description |
|---|---|---|
| Heart rate | bpm | Mean heart rate |
| Breathing rate | breaths/min | Mean breathing rate |
| RMSSD | ms | Root mean square of successive RβR interval differences |
| SDNN | ms | Standard deviation of normal-to-normal intervals |
| HF power | msΒ² | High-frequency HRV power |
| pNN50 | % | Percentage of successive RβR intervals differing by more than 50 ms |
Stored in:
processed/physiology/*
Vision Metrics
Blink-based measures were computed over non-overlapping 1-minute windows. Gaze and head-pose features were computed over non-overlapping 10-second windows to capture short-term changes in visual scanning behaviour.
Blink Metrics
| Metric | Unit | Description |
|---|---|---|
| Blink rate | blinks/min | Number of blink events per minute |
| Blink duration | s | Mean duration of detected blinks |
| PERCLOS | % | Percentage of time the eyes are closed |
Stored in:
processed/vision/*
Gaze Metrics
| Metric | Unit | Description |
|---|---|---|
| Gaze x std | rad | Variability of horizontal gaze angle |
| Gaze y std | rad | Variability of vertical gaze angle |
| Gaze speed | rad/s | Mean rate of gaze-angle change |
Head Pose Metrics
| Metric | Unit | Description |
|---|---|---|
| Yaw std | rad | Variability of yaw angle |
| Pitch std | rad | Variability of pitch angle |
| Roll std | rad | Variability of roll angle |
| Head speed | rad/s | Mean rate of head-pose change |
Stored in:
processed/vision/*
Loading the Dataset
Each HDF5 file corresponds to a single driving session.
A session can be loaded using h5py:
import h5py
with h5py.File("s027_2026-05-08_session.h5", "r") as f:
radar = f["raw/radar/beamformed_cube"]
physiology = f["raw/physiology"]
vision = f["raw/vision"]
Intended Use
This dataset is intended for research on:
- driver vigilance estimation
- physiological state monitoring
- multimodal learning
- radar-based non-contact sensing
- joint behavioural and physiological analysis
- longitudinal comparison of pre-drive and post-drive vigilance outcomes
Citation
If you use this dataset in your research, please cite the associated publication (citation to be added).
License
This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
For the full license text, see: https://creativecommons.org/licenses/by/4.0/
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