Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads
Paper • 2602.16870 • Published
Dataset Viewer
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: CastError
Message: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 470 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 458 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, episode_tag: string
child 6, duration_s: double
child 7, message_count: int64
child 8, channel_count: int64
child 9, topics: list<item: string>
child 0, item: string
child 10, schemas: list<item: string>
child 0, item: string
child 11, has_image: bool
child 12, has_pointcloud: bool
child 13, has_gps: bool
child 14, has_boxes: bool
child 15, has_aeva: bool
child 16, has_dmu_imu: bool
child 17, has_aeva_imu: bool
child 18, episode_type: string
child 19, dataset_variant: string
child 20, sequence_id: string
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_
...
truct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
last_modified_at: struct<$date: string>
child 0, $date: string
frame_fields: list<item: null>
child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
samples: list<item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: (... 470 chars omitted)
child 0, item: struct<_id: struct<$oid: string>, filepath: string, tags: list<item: null>, _media_type: string, _ra (... 458 chars omitted)
child 0, _id: struct<$oid: string>
child 0, $oid: string
child 1, filepath: string
child 2, tags: list<item: null>
child 0, item: null
child 3, _media_type: string
child 4, _rand: double
child 5, episode_tag: string
child 6, duration_s: double
child 7, message_count: int64
child 8, channel_count: int64
child 9, topics: list<item: string>
child 0, item: string
child 10, schemas: list<item: string>
child 0, item: string
child 11, has_image: bool
child 12, has_pointcloud: bool
child 13, has_gps: bool
child 14, has_boxes: bool
child 15, has_aeva: bool
child 16, has_dmu_imu: bool
child 17, has_aeva_imu: bool
child 18, episode_type: string
child 19, dataset_variant: string
child 20, sequence_id: string
child 21, _dataset_id: struct<$oid: string>
child 0, $oid: string
child 22, created_at: struct<$date: string>
child 0, $date: string
child 23, last_modified_at: struct<$date: string>
child 0, $date: string
group_media_types: null
info: null
app_config: null
classes: null
mask_targets: null
default_mask_
...
truct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: (... 318 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: string, subfield: string, fields: list<item: (... 306 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: string
child 3, subfield: string
child 4, fields: list<item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list (... 115 chars omitted)
child 0, item: struct<name: string, ftype: string, embedded_doc_type: null, subfield: null, fields: list<item: null (... 103 chars omitted)
child 0, name: string
child 1, ftype: string
child 2, embedded_doc_type: null
child 3, subfield: null
child 4, fields: list<item: null>
child 0, item: null
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
child 5, db_field: string
child 6, description: null
child 7, info: null
child 8, read_only: bool
child 9, created_at: struct<$date: string>
child 0, $date: string
last_modified_at: struct<$date: string>
child 0, $date: string
frame_fields: list<item: null>
child 0, item: null
to
{'_id': {'$oid': Value('string')}, 'name': Value('string'), 'slug': Value('string'), 'version': Value('string'), 'created_at': {'$date': Value('string')}, 'last_modified_at': {'$date': Value('string')}, 'last_deletion_at': Value('null'), 'last_loaded_at': {'$date': Value('string')}, 'sample_collection_name': Value('string'), 'persistent': Value('bool'), 'media_type': Value('string'), 'group_media_types': Json(decode=True), 'tags': List(Value('null')), 'info': Json(decode=True), 'app_config': {'dynamic_groups_target_frame_rate': Value('int64'), 'grid_media_field': Value('string'), 'media_fallback': Value('bool'), 'media_fields': List(Value('string')), 'modal_media_field': Value('string'), 'plugins': Json(decode=True)}, 'classes': Json(decode=True), 'default_classes': List(Value('null')), 'mask_targets': Json(decode=True), 'default_mask_targets': Json(decode=True), 'skeletons': Json(decode=True), 'camera_intrinsics': Json(decode=True), 'static_transforms': Json(decode=True), 'sample_fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('string'), 'subfield': Value('string'), 'fields': List({'name': Value('string'), 'ftype': Value('string'), 'embedded_doc_type': Value('null'), 'subfield': Value('null'), 'fields': List(Value('null')), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'db_field': Value('string'), 'description': Value('null'), 'info': Value('null'), 'read_only': Value('bool'), 'created_at': {'$date': Value('string')}}), 'frame_fields': List(Value('null')), 'saved_views': List(Value('null')), 'workspaces': List(Value('null')), 'annotation_runs': Json(decode=True), 'brain_methods': Json(decode=True), 'evaluations': Json(decode=True), 'runs': Json(decode=True), 'active_label_schemas': List(Value('null')), 'label_schemas': Json(decode=True), 'frame_label_schemas': Json(decode=True)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for Boreas Multimodal (MCAP)
A FiftyOne build of Boreas and Boreas Road Trip (Boreas-RT), the multi-season and multi-route autonomous driving datasets from the Autonomous Space Robotics Laboratory (ASRL) at UTIAS. This build repackages 3 driving sequences and 6 object-detection windows as time-synchronized MCAP recordings for FiftyOne's native multimodal dataset support (FiftyOne 1.19+). Each sample is one episode, viewable in FiftyOne's tiled multimodal viewer with synchronized camera, 128-beam lidar point cloud, decoded 360° radar imagery, GPS/map track, and IMU/encoder/velocity telemetry — plus, for the object-detection episodes, 3D bounding boxes rendered natively in the 3D tile and as 2D wireframe overlays on the camera and radar images.
Boreas/Boreas-RT are raw multi-sensor datasets for benchmarking
odometry/localization algorithms across seasons, weather, and road types;
only one recording (boreas-objects-v1) carries 3D object annotations
(Car/Pedestrian/Cyclist/Misc). This repackaging does not add or alter any
ground truth; see Dataset Creation for exactly what
was kept, converted, and left out.
This is a FiftyOne dataset with 8 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/boreas-multimodal")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
Boreas is a multi-season autonomous driving dataset collected by repeatedly
driving a route near the University of Toronto over one full year (Nov
2020-Nov 2021), capturing sun, rain, falling snow, and every season in
between — 44 public sequences, 350 km total. Boreas Road Trip (Boreas-RT)
extends this with 9 new, more diverse routes (suburbs, industrial, urban,
forest, farm, tunnel, skyway, regional, freeway), each repeated on average
6 times — 60 sequences, 643 km total — adding an Aeva Aeries II FMCW
Doppler lidar (32 of 60 sequences) and a standalone Silicon Sensing DMU41
IMU on top of the original sensor suite (128-beam Velodyne Alpha-Prime
lidar, 360° Navtech scanning radar, 5MP FLIR Blackfly S camera, Applanix
POS LV GNSS-INS). A separate, dedicated recording (boreas-objects-v1,
~104 minutes, sunny weather only) provides 326,180 3D object-detection
annotations (Car/Pedestrian/Cyclist/Misc) across 7,111 labeled lidar
frames. This FiftyOne build covers 2 driving-sequence episodes (one
original Boreas, one Boreas-RT) and 6 object-detection windows carved from
boreas-objects-v1 (see Curation Rationale).
- Curated by: Autonomous Space Robotics Laboratory (ASRL), University
of Toronto Institute for Aerospace Studies (UTIAS), in collaboration with
Applanix/Trimble — original Boreas/Boreas-RT data collection, sensor
calibration, and ground-truth pose post-processing. 3D object annotations
for
boreas-objects-v1were produced by the Scale.ai data annotation service. This MCAP/FiftyOne multimodal repackaging (episode authoring, dataset card) was prepared independently by Harpreet Sahota. - Funded by: Natural Sciences and Engineering Research Council of Canada (NSERC); Applanix/Trimble (co-authorship and sensor support); General Motors (vehicle donation); hosting via the Amazon Open Data Sponsorship Program.
- Shared by: Harpreet Sahota (this repackaging); the original Boreas
and Boreas-RT datasets are shared by ASRL via the AWS Open Data
Registry (
s3://boreas) and https://www.boreas.utias.utoronto.ca. - Language(s): N/A (sensor data — camera, lidar, radar, GPS, IMU; no text).
- License: Unknown — the AWS Open Data Registry listing's License field
is blank, and no license is stated in the
pyboreasdevkit or bucket contents at the time of this card; see Citation for the registry's suggested access citation.
Dataset Sources
- Repository (devkit): https://github.com/utiasASRL/pyboreas
- Paper: Burnett, K., Yoon, D. J., Wu, Y., Li, A. Z., Zhang, H., Lu, S., Qian, J., Tseng, W.-K., Lambert, A., Leung, K. Y. K., Schoellig, A. P., & Barfoot, T. D. (2023). Boreas: A Multi-Season Autonomous Driving Dataset. The International Journal of Robotics Research, 42(1-2), 33-42. arXiv:2203.10168; Lisus, D., Papais, K. M., Le Gentil, C., Preston-Krebs, E., Lambert, A., Leung, K. Y. K., & Barfoot, T. D. (2026). Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads. arXiv:2602.16870
- Demo: https://www.boreas.utias.utoronto.ca (official docs/download/ leaderboard site) and https://registry.opendata.aws/boreas/ (AWS Open Data listing)
Uses
Direct Use
- Exercising/demoing FiftyOne's multimodal MCAP support: synchronized playback of camera, 128-beam lidar point cloud, decoded 360° radar imagery, GPS/map track, and IMU/encoder/velocity telemetry across real autonomous-driving recordings.
- Inspecting 3D object-detection ground truth (Car/Pedestrian/Cyclist
boxes) rendered natively as
foxglove.SceneUpdatecuboids in the 3D tile, plus 2D wireframe overlays on the camera and radar bird's-eye-view images (both carrying label/uuid/numPoints metadata, inspectable via FiftyOne's built-in click-to-inspect panel), across 6 diverse time windows spread through the ~104-minuteboreas-objects-v1recording. - Prototyping odometry/localization analyses against dense ENU-frame
ground-truth pose (
/tf,/odom,/gps,/velocity) at each sensor's native timestamp. - Comparing sensor suites between the original Boreas platform and the Boreas-RT extension (adds an Aeva FMCW Doppler lidar and a standalone DMU41 IMU).
Out-of-Scope Use
- Reproducing the official odometry/localization/detection leaderboards at
boreas.utias.utoronto.ca — those benchmarks run over full, un-windowed
sequences (44 Boreas + 60 Boreas-RT sequences, each 90-150+ GB raw); this
build covers only 2 driving sequences (each a 10-second window, not the
full sequence) plus 6 short windows carved from the single
boreas-objects-v1recording. - Training or evaluating on
obj_test-split windows for detection — ground truth for that split is withheld by the source dataset for leaderboard fairness (confirmed directly: everyobj_testwindow checked has zero label files); onlyobj_trainwindows are included here. - Any task requiring depth maps, semantic/instance segmentation, or an HD
map layer — none exist anywhere in the source dataset (the devkit's own
BoreasDataset.mapattribute is an unimplementedTODO).
Dataset Structure
This is a flat (ungrouped) FiftyOne dataset with media_type: "multimodal"
and 8 samples. Each sample is one episode, stored as one .mcap
file; FiftyOne infers the multimodal media type automatically from the
.mcap extension. There are no separate per-frame image or point-cloud
samples — the episode is the sample unit, and every stream inside it
(camera, lidar, radar, GPS, IMU, encoder, and — where present — 3D box
annotations) is decoded live by FiftyOne's multimodal viewer.
The dataset carries no per-sample tags and dataset.info is empty (no
extra dataset-level metadata beyond the per-sample fields below).
Episodes in this dataset
sequence_id |
episode_tag |
dataset_variant |
episode_type |
Duration | has_boxes |
has_aeva |
has_gps |
|---|---|---|---|---|---|---|---|
boreas-2021-09-02-11-42 |
ep000 |
Boreas | odometry | 10.0s | False | False | True |
boreas-2024-12-03-12-54 |
ep000 |
Boreas-RT | odometry | 10.0s | False | True | True |
boreas-objects-v1 |
ep000_objsample |
Boreas | object_detection | 19.5s | True | False | False |
boreas-objects-v1 |
ep001_objtrain |
Boreas | object_detection | 19.5s | True | False | False |
boreas-objects-v1 |
ep002_objtrain |
Boreas | object_detection | 39.6s | True | False | False |
boreas-objects-v1 |
ep003_objtrain |
Boreas | object_detection | 45.7s | True | False | False |
boreas-objects-v1 |
ep004_objtrain |
Boreas | object_detection | 19.5s | True | False | False |
boreas-objects-v1 |
ep005_objtrain |
Boreas | object_detection | 19.7s | True | False | False |
The 2 driving episodes are fixed 10-second windows starting mid-sequence
(not full sequences — see Curation Rationale). The 6
boreas-objects-v1 episodes are 5 obj_train windows plus the devkit's own
obj_sample window, spread across the full ~104-minute recording for
scene diversity; ep000_objsample's window is also one of the obj_train
windows. boreas-objects-v1 has no applanix/imu.csv/dmi.csv/lat-lon
columns at all (an older, 2020-era recording — see Parsing
decisions), hence has_gps is False for all 6.
Fields
| Field | FiftyOne type | Description |
|---|---|---|
filepath |
StringField |
Absolute path to the episode's .mcap file — the sample's multimodal media |
sequence_id |
StringField |
Raw Boreas/Boreas-RT sequence ID (boreas-YYYY-MM-DD-HH-MM, or boreas-objects-v1), verbatim from the S3 folder name |
episode_tag |
StringField |
Local identifier disambiguating multiple episodes from the same sequence_id (e.g. the 6 different windows into boreas-objects-v1) |
episode_type |
StringField |
"odometry" or "object_detection" |
dataset_variant |
StringField |
"Boreas" or "Boreas-RT" |
duration_s |
FloatField |
Episode duration in seconds, computed from the MCAP's message-time span |
message_count |
IntField |
Total MCAP message count across all channels in the episode |
channel_count |
IntField |
Total MCAP channel (topic) count in the episode (10 for driving/detection episodes, 13 for the Boreas-RT episode) |
topics |
ListField(StringField) |
Every MCAP topic present (see MCAP topics below) |
schemas |
ListField(StringField) |
Every distinct Foxglove schema present in the episode |
has_image |
BooleanField |
Whether the episode has an Image-tile-decodable stream (foxglove.CompressedImage or foxglove.RawImage) — True for every episode |
has_pointcloud |
BooleanField |
Whether it has a decodable point-cloud stream for the 3D tile (foxglove.PointCloud) — True for every episode |
has_gps |
BooleanField |
Whether it has a decodable GPS fix stream for the Map tile (foxglove.LocationFix) — True only for the 2 driving episodes |
has_boxes |
BooleanField |
Whether it has a foxglove.SceneUpdate (3D box) stream — True only for the 6 boreas-objects-v1 episodes |
has_aeva |
BooleanField |
Whether it has an Aeva FMCW lidar point-cloud stream (/aeva) — True only for the Boreas-RT episode |
has_dmu_imu |
BooleanField |
Whether it has the standalone DMU41 IMU stream (/dmu_imu) — True only for the Boreas-RT episode |
has_aeva_imu |
BooleanField |
Whether it has the Aeva built-in IMU stream (/aeva_imu) — True only for the Boreas-RT episode |
Standard FiftyOne bookkeeping fields (id, metadata, created_at,
last_modified_at) are also present but not source-specific.
MCAP topics (inside each episode)
| Topic(s) | Schema | Tile | Notes |
|---|---|---|---|
/camera |
foxglove.CompressedImage (png) |
Image | Rectified, radially-undistorted, and pre-anonymized (faces/plates blurred) by the original dataset |
/camera/calibration |
foxglove.CameraCalibration |
(enables reprojection) | Static, logged once from P_camera.txt + real lens distortion from camera0_intrinsics.yaml (when present) |
/lidar |
foxglove.PointCloud |
3D | Velodyne Alpha-Prime, x,y,z,intensity (float32) |
/aeva (Boreas-RT episode only) |
foxglove.PointCloud |
3D | Aeva Aeries II FMCW lidar, x,y,z,intensity,radial_velocity (float32) |
/radar |
foxglove.RawImage (mono8) |
Image | Decoded polar→Cartesian bird's-eye-view conversion (no native polar-sensor MCAP schema exists); Boreas-RT radar additionally gets Doppler/offset correction before conversion |
/tf |
foxglove.FrameTransform |
(enables 3D) | enu_ref→{camera,lidar,radar,aeva} per-sensor pose, at each sensor's own native timestamp (sensors are not hardware-synchronized) |
/odom |
foxglove.Odometry |
3D + Plot | Lidar-frame pose + ENU/body-frame velocity |
/velocity |
generic JSON | Plot | Same velocity/body-rate values as /odom, as a flat dict for convenient Plot-tile inspection |
/gps (driving episodes only) |
foxglove.LocationFix |
Map | Lat/lon/heading, converted from the source CSV's radians to the degrees LocationFix expects (see Parsing decisions) |
/imu, /encoder (driving episodes only) |
generic JSON | Plot | Applanix-integrated IMU and wheel-encoder pulse count |
/dmu_imu, /aeva_imu (Boreas-RT episode only) |
generic JSON | Plot | Standalone DMU41 IMU and Aeva's built-in IMU |
/boxes (object-detection episodes only) |
foxglove.SceneUpdate |
3D | One SceneEntity per box (id = source uuid), CubePrimitive sized [length,width,height], colored by class, metadata carrying label/type/uuid/numPoints |
/camera/boxes, /radar/boxes (object-detection episodes only) |
foxglove.ImageAnnotations |
Image | 2D wireframe (camera) / bird's-eye-view footprint (radar) overlays of the same boxes, each carrying the same label/type/uuid/numPoints metadata |
Label types and why
No FiftyOne sample-level label fields (Detections, Detections3D,
etc.) are attached. Because each sample is a multi-second-to-minute
continuous recording rather than a single frame, there is no
single fixed-length list a sample-level label field could hold. Instead,
the 3D object annotations for the 6 boreas-objects-v1 episodes are
embedded as additional MCAP topics inside the same timeline as the sensor
data (/boxes, /camera/boxes, /radar/boxes — see MCAP
topics), decoded live by FiftyOne's
multimodal viewer alongside the camera/lidar/radar tiles, exactly like the
sensor topics themselves. The has_boxes sample field exists so episodes
can be filtered without opening every MCAP file first, e.g.
dataset.match(F("has_boxes")).
Parsing decisions
- One sample = one episode. Full Boreas/Boreas-RT sequences are 90-150+ GB raw each (44+60 = 104 sequences total), so only 2 driving episodes (one original Boreas, one Boreas-RT) were authored, each a fixed 10-second window starting mid-sequence rather than the full recording.
- Object-detection episode selection.
boreas-objects-v1is one continuous ~104-minute recording; the source dataset's own detection benchmark carves it into 53 short (17.6-71.9s) named windows (obj_train: 37,obj_test: 16) rather than splitting by whole sequence. 6 windows were selected fromobj_train(spread across the full recording for scene diversity) —obj_testwindows were excluded because their ground truth is withheld by the source dataset for leaderboard fairness (confirmed directly: everyobj_testwindow checked has zerolabels_detection/*.txtfiles in range, while everyobj_trainwindow checked has labels). - Boxes with
numPoints == 0are dropped, matching the source devkit's ownobject_detection.ipynbtutorial (BoundingBoxes.filter_empty()), which always removes boxes with no supporting lidar returns before display. Had no effect on the specific windows included here (0 of 4,896 boxes checked havenumPoints == 0), but the authoring script is defensive against it for other windows. - 3D boxes are logged at every lidar frame (10 Hz), not just directly
labeled ones (5 Hz) — the source only labels every other lidar frame
directly; boxes on the in-between frames are produced via the devkit's
own SO(3) interpolation (
BoundingBoxes.interpolate(), needs theasrl-pylgmathpackage), the same codepath the devkit itself uses for camera/radar frames (which never land on a directly-labeled timestamp). This is a deliberate scope choice beyond the devkit's own tutorial, which only ever shows directly-labeled lidar frames. - Box dimension order is
[length, width, height](verified directly in the source paper's text: "(dx,dy,dz) represent the bounding box dimensions (length, width, and height)") — thereadme.txtshipped alongside the label files in the S3 bucket incorrectly states the order asheight, width, length; this build follows the paper, not the bucket'sreadme.txt. gps_post_process.csv'slatitude/longitude/headingare in radians, not degrees (like the siblingroll/pitchcolumns, used directly with no conversion elsewhere in the devkit) — converted to degrees forfoxglove.LocationFix.latitude/longitude(which expect degrees), whileheadingis passed straight through unconverted (it's already radians, matchingLocationFix.heading's expected units).- Angular velocity columns in every pose CSV are stored in
z,y,xorder, notx,y,z— an intentional, paper-documented convention ("the flipped ordering... for backward compatibility"), not a bug; reordered tox,y,zbefore logging. - Aeva point fields are
[x,y,z,radial_velocity,intensity,signal_quality, reflectivity,time,point_flags](9 named fields,point_flagsoccupying 2 of the 10 float32 slots as one float64) — this exact order is confirmed by the Boreas-RT paper's text, resolving an internal inconsistency in the devkit itself (a generic per-point-time helper assumes the last column is always "time", which is only true for the 6-field Velodyne format, not the 10-field Aeva format). - Radar Doppler/offset correction is applied only to the Boreas-RT
episode, gated on the devkit's own
radar_chirp_type_upgrade_timeconstant — applying it to the original (pre-upgrade) Boreas radar would use a bogus chirp-type value (that sensor generation has no real per- azimuth chirp data) and corrupt the image. - Sensors are not hardware-synchronized — real per-sensor timestamps
are used as-is for
/tf, which is correct/expected behavior per both source papers, not an alignment bug.
Dataset Creation
Curation Rationale
Full Boreas/Boreas-RT sequences are far too large for a lightweight
FiftyOne showcase — 104 sequences total (44 Boreas + 60 Boreas-RT), each
90-150+ GB raw, plus a separate 152 GB standalone recording for object
detection. Rather than attempting exhaustive coverage, this build selects:
one original-Boreas driving episode and one Boreas-RT driving episode
(each a 10-second window, chosen to demonstrate the respective sensor
suites — the Boreas-RT one specifically for its Aeva FMCW lidar and dual
IMU streams), plus 6 object-detection windows spread across the full
boreas-objects-v1 recording for scene diversity, all drawn from the
already-labeled obj_train split.
Source Data
Data Collection and Processing
Per ASRL's published papers and devkit documentation: Boreas was collected
by driving a repeated route (the "Glen Shields" route) near the University
of Toronto over one year (Nov 2020-Nov 2021), captured in every season and
weather condition including falling snow — 44 public sequences, 350 km
total. Sensor suite: a 128-beam Velodyne Alpha-Prime lidar (360°×40° FOV,
2.2M points/s, 10 Hz), a 360° Navtech CIR304-H scanning radar (0.9°
angular resolution, 0.0596 m/200 m range pre-upgrade or 0.0438 m/250 m
range post-upgrade, 4 Hz), a 5MP FLIR Blackfly S camera (2448×2048, 81°×71°
FOV, 10 Hz, images pre-rectified and pre-anonymized for faces/license
plates), and an Applanix POS LV 220 GNSS-INS (200 Hz, 2-4 cm RTX accuracy)
providing centimetre-level post-processed ground-truth pose in a
fixed ENU frame anchored to the first pose of the first sequence. A
dedicated standalone recording, 104 minutes, sunny
weather), was separately annotated by the Scale.ai data annotation service
with 326,180 3D object boxes (Car/Pedestrian/Cyclist/Misc) across 7,111
labeled lidar frames at 5 Hz.boreas-objects-v1 (
Boreas Road Trip (Boreas-RT) extends the same collection methodology to 9 new routes across suburban, industrial, urban, rural, and highway environments (60 sequences, 643 km total), adding an Aeva Aeries II FMCW Doppler lidar (120°×30° FOV, ~1.0M points/s, 32 of 60 sequences) and a standalone Silicon Sensing DMU41 IMU (200 Hz) alongside the original sensor suite, plus Doppler-capable radar firmware (per-azimuth "chirp type" metadata enabling Doppler velocity extraction and undistortion).
For this repackaging: raw per-frame sensor files, calibration, and
ground-truth CSVs were downloaded from the public, unsigned s3://boreas
bucket (targeted time-windowed slices, not full sequences), decoded using
the official pyboreas devkit (with asrl-pylgmath/asrl-pysteam added to
unblock its 3D box interpolation code path, and foxglove-sdk for MCAP
authoring), cross-checked byte-for-byte against real downloaded files
rather than trusted from documentation alone, and packed into one .mcap
file per episode. No sensor data or ground truth was synthesized,
relabeled, or altered beyond the conversions documented in Parsing
decisions.
Who are the source data producers?
Autonomous Space Robotics Laboratory (ASRL), University of Toronto Institute for Aerospace Studies (UTIAS), in collaboration with Applanix/Trimble — original Boreas/Boreas-RT data collection, sensor calibration, and ground-truth pose post-processing.
Annotations
Annotation process
3D bounding boxes for boreas-objects-v1 were produced by the Scale.ai
data annotation service, per the original Boreas paper: 7,111 lidar frames
were labeled at 5 Hz (double the labeling rate is achievable via
interpolation, since the lidar itself runs at 10 Hz — see Parsing
decisions), yielding 326,180 unique 3D box
annotations across 4 classes (Car, Pedestrian, Cyclist, Misc), divided into
53 continuous scenes (20-70s each) and split into 37 training scenes and 16
test scenes (test-scene labels withheld for the official benchmark). No
other annotation exists anywhere in either dataset — odometry/localization
ground truth is automated GNSS/IMU/wheel-encoder post-processing, not
manual annotation.
Who are the annotators?
The Scale.ai data annotation service, for the boreas-objects-v1 3D box
annotations only. All other "ground truth" in this dataset (pose,
velocity) is produced automatically by Applanix's POSPac software suite
from GNSS/IMU/wheel-encoder measurements plus an RTX correction
subscription — no manual annotation is involved.
Personal and Sensitive Information
Camera images are pre-anonymized by the original dataset producers: faces and license plates are blurred using the "Understand.ai Anonymizer" tool before public release, per the original Boreas paper. This repackaging performs no additional processing, re-identification, or redaction beyond what ASRL already released publicly.
Citation
BibTeX:
@article{burnett_ijrr23,
author = {Keenan Burnett and David J Yoon and Yuchen Wu and Andrew Z Li
and Haowei Zhang and Shichen Lu and Jingxing Qian and
Wei-Kang Tseng and Andrew Lambert and Keith YK Leung and
Angela P Schoellig and Timothy D Barfoot},
title = {Boreas: A multi-season autonomous driving dataset},
journal = {The International Journal of Robotics Research},
volume = {42},
number = {1-2},
pages = {33-42},
year = {2023},
doi = {10.1177/02783649231160195}
}
@misc{lisus_brrt26,
author = {Daniil Lisus and Katya M. Papais and Cedric Le Gentil
and Elliot Preston-Krebs and Andrew Lambert and
Keith Y. K. Leung and Timothy D. Barfoot},
title = {Boreas Road Trip: A Multi-Sensor Autonomous Driving
Dataset on Challenging Roads},
year = {2026},
eprint = {2602.16870},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2602.16870}
}
APA:
Burnett, K., Yoon, D. J., Wu, Y., Li, A. Z., Zhang, H., Lu, S., Qian, J., Tseng, W.-K., Lambert, A., Leung, K. Y. K., Schoellig, A. P., & Barfoot, T. D. (2023). Boreas: A multi-season autonomous driving dataset. The International Journal of Robotics Research, 42(1-2), 33-42.
Lisus, D., Papais, K. M., Le Gentil, C., Preston-Krebs, E., Lambert, A., Leung, K. Y. K., & Barfoot, T. D. (2026). Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads. arXiv:2602.16870.
"Boreas Autonomous Driving Dataset was accessed on
DATEfrom https://registry.opendata.aws/boreas." — suggested citation text per the AWS Open Data Registry listing.
More Information
This repository is an independently-curated, derived subset of the
official Boreas/Boreas-RT datasets, repackaged as MCAP for FiftyOne's
multimodal support. It is not an official ASRL/UTIAS artifact. For the
full datasets (all 104 driving sequences, the full boreas-objects-v1
recording, the devkit, and the live odometry/localization/detection
leaderboards), see https://www.boreas.utias.utoronto.ca,
https://registry.opendata.aws/boreas/, and
https://github.com/utiasASRL/pyboreas.
Dataset Card Authors
Harpreet Sahota (@harpreetsahota) — MCAP repackaging and this card. Original dataset producers are listed under Dataset Description.
Dataset Card Contact
Harpreet Sahota — https://huggingface.co/harpreetsahota
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