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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
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 lengths

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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:

/
β”œβ”€β”€ raw/
β”‚   β”œβ”€β”€ radar/
β”‚   β”‚   └── beamformed_cube
β”‚   β”œβ”€β”€ physiology/
β”‚   β”‚   β”œβ”€β”€ hr_1hz
β”‚   β”‚   β”œβ”€β”€ br_1hz
β”‚   β”‚   β”œβ”€β”€ ibi
β”‚   β”‚   └── ibi_ts
β”‚   β”œβ”€β”€ vision/
β”‚   β”‚   β”œβ”€β”€ landmarks_raw
β”‚   β”‚   β”œβ”€β”€ gaze_xy
β”‚   β”‚   β”œβ”€β”€ pose_rxyz
β”‚   β”‚   β”œβ”€β”€ eye_open
β”‚   β”‚   └── confidence
β”‚   └── labels/
β”‚       β”œβ”€β”€ motion_flag
β”‚       β”œβ”€β”€ predrive
β”‚       └── postdrive
└── processed/
    β”œβ”€β”€ physiology/
    β”‚   β”œβ”€β”€ mean_hr
    β”‚   β”œβ”€β”€ mean_br
    β”‚   β”œβ”€β”€ rmssd
    β”‚   β”œβ”€β”€ sdnn
    β”‚   β”œβ”€β”€ pnn50
    β”‚   └── hf
    └── vision/
        β”œβ”€β”€ blink_rate
        β”œβ”€β”€ perclos
        β”œβ”€β”€ blink_duration
        β”œβ”€β”€ gaze_speed
        β”œβ”€β”€ gaze_x_std
        β”œβ”€β”€ gaze_y_std
        β”œβ”€β”€ head_speed
        β”œβ”€β”€ head_yaw_std
        β”œβ”€β”€ head_pitch_std
        └── 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 R–R interval differences
SDNN ms Standard deviation of normal-to-normal intervals
HF power msΒ² High-frequency HRV power
pNN50 % Percentage of successive R–R 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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