Datasets:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'radar_available', 'lidar_available', 'hour_of_day', 'split', 'country', 'month'})
This happened while the csv dataset builder was generating data using
hf://datasets/Trazemag/PRECOG-Labels/precog_dataset.csv (at revision ac8e48ac0560e6168a7ddd3169d2548fd1495592), ['hf://datasets/Trazemag/PRECOG-Labels@ac8e48ac0560e6168a7ddd3169d2548fd1495592/danger_labels.csv', 'hf://datasets/Trazemag/PRECOG-Labels@ac8e48ac0560e6168a7ddd3169d2548fd1495592/precog_dataset.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
clip_id: string
is_danger: bool
min_ttc: double
country: string
month: int64
hour_of_day: int64
radar_available: bool
lidar_available: bool
split: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1305
to
{'clip_id': Value('string'), 'is_danger': Value('bool'), 'min_ttc': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1361, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 940, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 6 new columns ({'radar_available', 'lidar_available', 'hour_of_day', 'split', 'country', 'month'})
This happened while the csv dataset builder was generating data using
hf://datasets/Trazemag/PRECOG-Labels/precog_dataset.csv (at revision ac8e48ac0560e6168a7ddd3169d2548fd1495592), ['hf://datasets/Trazemag/PRECOG-Labels@ac8e48ac0560e6168a7ddd3169d2548fd1495592/danger_labels.csv', 'hf://datasets/Trazemag/PRECOG-Labels@ac8e48ac0560e6168a7ddd3169d2548fd1495592/precog_dataset.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
clip_id string | is_danger bool | min_ttc null |
|---|---|---|
01d3588e-bca7-4a18-8e74-c6cfe9e996db | false | null |
038678de-6307-4cd2-a3c9-5d64e1fb8862 | false | null |
0391b4a7-3956-4146-ad2e-98b74e7877fa | false | null |
0397f34a-b3f3-4e40-ac2b-a9fcd656226c | false | null |
04c5a3f4-7d2f-499a-ba05-b57194594735 | false | null |
054da32b-9f3d-4074-93ab-044036b679f8 | true | null |
058bf960-f531-4ae6-8b24-455d7683676e | true | null |
0853ef8f-9d6b-4963-8ea3-fafb7e107ac7 | false | null |
0931a850-4303-47bc-a041-4e17b4e36c2b | false | null |
0a948f59-0a06-41a2-8e20-ac3a39ff4d61 | false | null |
0abe118e-aa79-41f6-a719-f2df8abaf1ea | false | null |
0c8731a8-9cd4-4f59-9603-165b1ed53e07 | false | null |
0dd3eb7f-47a1-40d5-a228-d06ea1288839 | false | null |
120db5db-56d4-43b5-9453-396b9c82fb7d | false | null |
1230b185-bca6-412f-8c90-f43a6529acb2 | false | null |
15215a0a-22c8-4894-93a0-0d91fbf73a86 | false | null |
16e0deee-e8d7-414a-8bf0-c22feda87875 | false | null |
186504a4-04fc-4609-96be-0c1f5e7096e5 | false | null |
1f47bf7f-d233-480c-b166-7512d8e9ac97 | false | null |
1f7ef408-e2fc-4f9c-9a54-c81fbb854141 | false | null |
1fcf2fef-279b-4490-adc6-9b7d1ff0118d | false | null |
2357ac16-82a9-458b-81ea-e077f2b92feb | false | null |
259b1529-0d28-4ea5-b822-d8691e91f062 | false | null |
25cd4769-5dcf-4b53-a351-bf2c5deb6124 | false | null |
26f8dc7e-a9a2-4302-b847-af2009abf677 | false | null |
275c9acf-8cb4-46ca-8335-5a7b80a501fe | false | null |
2a48843a-2eed-4c21-94c2-1de716605b7d | false | null |
2edf278f-d5e3-4b83-b5df-923a04335725 | false | null |
30f3d243-f26e-43f9-a63a-ac5d1b115d15 | false | null |
37c25a1d-15a1-417e-9364-a6305a166423 | true | null |
38a3f022-ea21-4735-8e87-7a8faa63089c | false | null |
3c16fed6-60c0-4cab-9b24-ac1af36c9c99 | false | null |
3f09a86a-3350-4c78-b9ed-c0024b321a46 | false | null |
402fdeb6-f078-44af-a9b3-79bfa15961a1 | false | null |
43ada34d-283f-46b9-ae38-cd41c576b889 | false | null |
43c43f34-dad1-4357-bd23-dafc954ae4f0 | false | null |
46003675-4b4e-4c0f-ae54-3f7622bddf6a | true | null |
49d2a3f1-de77-41c4-9ca5-6c997e9fc129 | false | null |
4d7920f8-98f1-4b48-95e0-e0296fd593fc | false | null |
4db63321-8ec2-41ae-b9af-1743325bff91 | false | null |
5761344d-52d7-4278-afee-16dc2dc948f7 | false | null |
58a6c538-8a4d-4c6d-97fa-d217474b8046 | false | null |
5919aa25-d197-43fe-bdfd-38460ab1e2ff | false | null |
5c8a7587-d850-474c-b297-7a633d0538d1 | false | null |
5e9a00f1-f575-43c8-a4e2-62e52a651dbb | false | null |
5f49a077-0d7d-4c7d-9c53-520fb353ead2 | false | null |
632e0c2c-19a4-4b9a-b684-f014c4aff51f | false | null |
694e4fe9-2d6f-4b76-abc9-7b036e46d6d2 | false | null |
69cf2443-29d4-46d3-b472-669b0aca43a3 | false | null |
69dfea29-acfa-4499-be99-53d06631ffb0 | false | null |
6e17da60-efe0-4cc2-803e-ee82975a784b | false | null |
72aa07bb-600f-434b-85ad-3afef477c364 | false | null |
73e0cb9f-6469-4768-b3a3-4f892c827b0d | false | null |
7555abef-c163-4263-b49e-31e773a4faef | false | null |
76f4a312-4b5a-4bce-8499-b249ae70d0a0 | false | null |
775ca1cd-4804-4d07-bff8-5b817ee0a65f | false | null |
78e29f63-96dd-4112-9c14-e3e9777cec90 | false | null |
79b9252b-a96a-4f43-bdc7-05ebd184d174 | false | null |
7a22a3d6-1156-427f-b8d6-ac379c6f9acb | false | null |
7bf7e1fe-341f-4325-a01a-5493bc9ca8c5 | false | null |
7e1bc82f-cc1c-457a-ac65-2ef907f2e3da | false | null |
7fdd76e1-fa59-47a8-87c5-81d1a66b7501 | false | null |
808039e3-5efb-4cc2-b845-93c65b99ddf8 | false | null |
86de1c0c-e9cd-44ef-aad2-211c6b8a00da | false | null |
8bfa4362-3cdf-49d4-8063-090f52771628 | false | null |
8d669f31-90e7-4828-a99b-264bf6dedd14 | false | null |
95ad1d0a-7713-4899-91dd-08176123823f | false | null |
9613c363-7d42-4680-ac8e-7341bca8aeaa | false | null |
987e082b-3d45-4223-bf61-3c59969f5de9 | false | null |
9dfc2cef-507f-45cc-bb3f-8a5b88e25ec0 | false | null |
a75e3cf8-e84e-4082-ab64-92d515bfda53 | false | null |
b3fa3a3e-dd0f-4808-a74a-df462b6fa53d | false | null |
b9b4ddc7-feb0-4749-9d14-931a70fe3e17 | false | null |
c3ca785e-3876-4a0a-a8bd-890f2027d795 | true | null |
c6915a10-9f22-464d-a422-15b8960a57d7 | true | null |
c9e6b82f-c3e9-4242-89e5-e62caaaeb9fe | false | null |
cbb31f9e-1bbc-4ea9-b20b-6974d4ae98ab | false | null |
cd43c62f-fd9f-4064-abb3-bfd926f39a65 | false | null |
ce5462a3-649e-401d-86ff-5f30c98da7d6 | false | null |
d0a0cbf0-bcc2-4c72-9a54-f542dc99f560 | false | null |
d81becb2-5333-4808-a01e-14ce2f2f1027 | false | null |
db2c5fbe-1903-4b8e-8df3-a7d02cff1893 | false | null |
dbcf2943-b7d6-4e14-9a5b-9f6a4966731d | false | null |
dc257402-687c-4a34-a950-8bb6a3c2508d | false | null |
dd1b6121-dec2-4163-adde-b3f09f0a66d8 | false | null |
dea8d46d-f228-4a21-969e-4a59daf8a981 | false | null |
dfb6db91-d361-4ede-a41e-1d5086b650f8 | false | null |
e030ea33-9d21-4e8d-9a1a-1d7a8d4f41a1 | false | null |
e13eaabc-6287-46ba-964c-548b7d5615b8 | false | null |
e762aaa3-3b35-4ff6-8b4a-55f05d4a57ee | false | null |
e9162b7f-00f1-4563-890e-15c9fc89ad7a | false | null |
ea5c5be6-5e18-4489-9784-8f73860f887b | false | null |
ecafce84-447d-43a6-aff1-b385fbc71f15 | false | null |
edd12c03-09d5-4f0d-bf6c-0025b4a9b56f | false | null |
ef4264ed-0fd2-4a64-9831-87e1aae28407 | false | null |
fa83bcb8-ea31-4dbb-b447-4fb458f5984b | false | null |
03560c1a-eac6-499d-bd81-90a19b8d66bd | false | null |
0494325b-8704-4aa9-939f-585f99fe97c7 | false | null |
064967a7-08a1-4cf0-b55b-5fcde3d91f67 | false | null |
09312f4a-c618-46a8-a8ff-1db37e043b5d | false | null |
End of preview.
PRECOG-Labels: PhysicalAI-AV Danger Labels
Author: Nikhil Upadhyay | MSc Business Analytics | Dublin Business School Project: PRECOG-AV
Overview
Physics-based danger labels for 298,326 clips from the NVIDIA PhysicalAI-AV dataset, mined using Time-to-Collision (TTC) analysis of 3D obstacle parquets. The first danger labelling at this geographic scale: 25 countries.
14,192 danger clips identified (4.76% positive rate)
Files
| File | Rows | Description |
|---|---|---|
danger_labels.csv |
298,326 | Binary danger flag and min TTC per clip |
precog_dataset.csv |
298,326 | Full metadata: country, split, hour, sensor availability |
Geographic Split
| Split | Countries | Clips |
|---|---|---|
| Train | 20 countries | 275,573 |
| Val | Austria, Finland, Portugal | 16,151 |
| Test | Greece, Bulgaria | 6,602 |
Key Findings
- Danger rates vary 4.7x across countries — Italy 9.9% vs Estonia 2.1%
- Peak danger window: 13:00 to 15:00 across all countries
- Mean danger window: 11.18 seconds across 2,177 labelled test clips
Citation
@misc{upadhyay2026precog,
title = {PRECOG: Proactive Risk and Environmental Cognition for Autonomous Vehicles},
author = {Upadhyay, Nikhil},
year = {2026},
url = {https://github.com/TrazeMaG/PRECOG-AV}
}
- Downloads last month
- 41