The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
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: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
DataSyndicate Multi-Environment Perception Edge Block (20-Video Test Suite)
📊 Overview
This dataset repository contains a curated, multi-scenario block of 20 raw physical edge-case video captures paired with structured JSON metadata sidecars. Designed specifically for autonomous vehicle, robotics, and computer vision teams, this block stresses-tests multi-object tracking (MOT) pipelines, foundation models, and vision-language-action (VLA) architectures against severe real-world optical phenomena.
Unlike synthetic simulations or clean laboratory sets, this package captures the raw, unvarnished physical chaos encountered on public roadways under diverse lighting and environmental stressors.
🔬 Captured Physical Edge-Case Categories
This 20-video suite isolates specific, high-failure optical challenges across daylight and nighttime operations:
- 🌞 Day Lens Flare: Direct solar saturation and chromatic aberration testing optical sensor limits and bounding box persistence under sudden glare.
- 🪞 Day & Night Mirrors: Complex multi-layer reflection confusion, tracking anomalies in side-view and rear-view mirrors, and spatial duplicate handling.
- ⚡ Day Strobe & Flashing Optics: High-intensity emergency strobes, flashing railway signals, and rolling-shutter artifacts that disrupt tracking confidence.
- 🚆 Day & Night Trains: Repeating geometric structures, long-sequence occlusion, and dense multi-car tracking challenges (e.g., track ID swapping and label flickering between cars, trucks, and roadside objects).
🔒 Privacy & Enterprise Compliance (Tier-2 Standards)
- Adaptive PII Scrubbing: All sensitive data (faces and close-range license plates) has been processed using distance-adaptive Gaussian blurring to ensure strict privacy compliance.
- Pristine ML Ingress: Video frames remain completely free of burned-in visual overlays or marketing boxes, keeping raw pixel telemetry intact for sandbox ingestion.
- Decoupled Sidecar Telemetry: Object tracking logs, bounding box coordinates, and classification data are exported independently into structured JSON metadata sidecars for each video.
🚀 Recommended Use Cases
- Model Validation & Stress Testing: Benchmark how custom perception stacks or open-source VLA models (such as Nvidia-style reasoning architectures) handle optical saturation, glare, and tracking loss.
- Fine-Tuning Datasets: Feed real-world failure modes back into training loops to patch generalization blind spots.
📄 Licensing & Commercial Use
Released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC-4.0) license. Free for evaluation, benchmarking, and non-commercial research. Commercial production integration requires an enterprise license agreement from XGEN AI LLC / DataSyndicate.
Captured, processed, and curated by XGEN AI LLC / DataSyndicate — Delivering unvarnished physical edge data for the autonomous future.
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