The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: Schema at index 1 was different:
level1: string
level2: string
level3: string
pic_name: string
pic_path: string
pre_dec_file: string
gt_json_file: string
is_longtail: string
Sup_description: string
lt_ele: string
acc_factors: string
COT: string
post_dec: string
is_transfer2p: string
vs
level1: string
level2: string
level3: string
pic_name: string
is_longtail: string
lt_ele: string
acc_factors: list<item: string>
Sup_description: string
post_dec: string
pic_path: string
pre_dec_file: string
gt_json_file: string
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 572, in _iter_arrow
yield new_key, pa.Table.from_batches(chunks_buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
level1: string
level2: string
level3: string
pic_name: string
pic_path: string
pre_dec_file: string
gt_json_file: string
is_longtail: string
Sup_description: string
lt_ele: string
acc_factors: string
COT: string
post_dec: string
is_transfer2p: string
vs
level1: string
level2: string
level3: string
pic_name: string
is_longtail: string
lt_ele: string
acc_factors: list<item: string>
Sup_description: string
post_dec: string
pic_path: string
pre_dec_file: string
gt_json_file: stringNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
RADIUS-Drive（及 RADIUS-Data）
本仓库在 Hugging Face 上发布 RADIUS-Drive（风险感知一致性诊断评测基准）。
This Hugging Face repo hosts RADIUS-Drive, a risk-aware diagnostic benchmark for safety-critical autonomous driving.
- GitHub: https://github.com/pupprtseven/RADIUS_Drive_Bench
- Paper:
Benchmark Overview
RADIUS-Drive 关注 Pseudo-Correctness（伪正确性）： 模型可能给出表面正确的驾驶决策，但未能识别、定位或验证潜在风险机制。
RADIUS-Drive targets Pseudo-Correctness, where models produce correct-looking driving decisions while failing to trigger, localize, or validate underlying risk factors.
为此，RADIUS-Drive 提出 SAR（Safety → Awareness → Reasoning）诊断协议， 通过跨阶段一致性指标，系统性评估模型在安全关键决策中的 风险感知与推理一致性。
To this end, RADIUS-Drive introduces the SAR (Safety → Awareness → Reasoning) diagnostic protocol, enabling consistency-centric evaluation of risk perception and decision reasoning.
任务 / Tasks
SAF（Safety）
检测模型是否能够识别并响应安全关键风险信号。
Detect whether the model correctly triggers safety awareness under risk-critical conditions.
AWR（Awareness）
评估模型是否能够定位并解释风险来源，而非仅给出结果。
Assess whether the model localizes and explains the source of risk, beyond outcome correctness.
REA（Reasoning）
验证模型的决策是否基于与风险一致的因果推理过程。
Verify whether decisions are supported by risk-consistent causal reasoning.
X-CONS（Cross-Phase Consistency）
检查 Safety、Awareness 与 Reasoning 之间是否存在一致性违背（Pseudo-Correctness）。
Evaluate cross-phase consistency to diagnose pseudo-correct behavior.
关键约定 / Key Conventions
所有样本均配备 可审计的 ground truth，支持reference-based injection 与 reference-free generation 两种场景构造方式。
All samples provide auditable ground truth, supporting both reference-based injection and reference-free generation.
评测结果应同时报告：
阶段内性能（per-phase）
跨阶段一致性指标（e.g., CF-CDA, Guess）
Results should report both per-phase scores and cross-phase consistency metrics.
Dataset Contents
Each instance X is released as an image plus JSON sidecars:
dataX.png: the rendered driving scene.dataX.json: taxonomy and post-decision supervision, including:- classification (Level-3)
lt_ele(dominant element)acc_factorspost_dec(reference level and/or plan text)
dataX_aligned.json: simulator-ready coarse state for Phase-1 Safety (map/relations/kinematics abstraction).dataX_gt.json: pre-decision action tags for Phase-1 scoring (per-action collision/hazard/safe, and optional best action).
Design Principles
The schema is intentionally minimal:
dataX_aligned.jsonis only as complex as needed for deterministic Safety rollout.dataX.jsoncarries the Phase-2/3 labels required for SAR diagnosis and cross-phase consistency metrics.
Directory Structure
RADIUS_DataSet550/
├── json/ # JSON sidecars for each instance
├── pic/ # Rendered scene images
├── aligned_dataset.py # Utility script for alignment
├── example.json
├── example_plot.png
└── README.md
Notes
- File naming follows the
dataX.*convention across image and JSON sidecars. - For benchmark usage, refer to the
RADIUS_benchmarkmodule documentation.
License
MIT License
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