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tangzx2024
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CoLT-Drive

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Visual Question Answering
Languages:
English
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1K<n<10K
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Tags:
autonomous-driving
counterfactual
long-tail
decision-making
vision-language-model
License:
Dataset card Files
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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:    CastError
Message:      Couldn't cast
sample_id: string
scene_id: string
scene_name: string
obstacle_id: string
obstacle_type: string
obstacle_category: string
position: string
version: string
navigation: string
has_lead: bool
history: list<item: struct<t: double, ego_speed: double, ego_accel: double>>
  child 0, item: struct<t: double, ego_speed: double, ego_accel: double>
      child 0, t: double
      child 1, ego_speed: double
      child 2, ego_accel: double
acceptable_pairs: list<item: struct<lon_action: string, lat_action: string>>
  child 0, item: struct<lon_action: string, lat_action: string>
      child 0, lon_action: string
      child 1, lat_action: string
to
{'acceptable_pairs': List({'lon_action': Value('string'), 'lat_action': Value('string')})}
because column names don't match
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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              sample_id: string
              scene_id: string
              scene_name: string
              obstacle_id: string
              obstacle_type: string
              obstacle_category: string
              position: string
              version: string
              navigation: string
              has_lead: bool
              history: list<item: struct<t: double, ego_speed: double, ego_accel: double>>
                child 0, item: struct<t: double, ego_speed: double, ego_accel: double>
                    child 0, t: double
                    child 1, ego_speed: double
                    child 2, ego_accel: double
              acceptable_pairs: list<item: struct<lon_action: string, lat_action: string>>
                child 0, item: struct<lon_action: string, lat_action: string>
                    child 0, lon_action: string
                    child 1, lat_action: string
              to
              {'acceptable_pairs': List({'lon_action': Value('string'), 'lat_action': Value('string')})}
              because column names don't match

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CoLT-Drive

A counterfactual long-tail benchmark for driving decisions. The driving context (road geometry, ego pose, navigation command, motion history) is held fixed and only the object in the scene changes. The benchmark asks whether a model's decision updates when it should and holds when it should not.

Task

Given a front-camera image and the driving context, output one longitudinal and one lateral action:

Axis Actions
Longitudinal keep_speed, slow_down, yield, creep, stop, speed_up
Lateral keep_lane, nudge_left, nudge_right

Each sample has an accepted set of action pairs (1 to 14 pairs, 3.5 on average). A prediction is correct if it is in the set.

Splits and composition

Both splits share identical target objects and road geometry and differ only in background traffic.

Split Samples Background traffic
vfull 1,768 naturalistic
vclean 1,768 background vehicles removed

50 obstacle types in five categories, grouped by the response they require:

Category Samples per split Expected behaviour
false_positive 435 preserve the nominal action
entity_living 348 yield or stop
road_hazard 348 adapt to the hazard
entity_nonliving 347 slow down or steer around
full_block 290 stop

Object position is controlled separately: center (1,159), full_lane_blockage (290), left_biased (145), right_biased (145), airborne (29).

Files 

vfull/
├── manifest.json          sample ids in order
└── <sample_id>/
    ├── front_camera.jpg
    ├── prompt.txt         driving context shown to the model
    ├── gt.json            accepted action pairs
    └── meta.json          obstacle type, category, position

gt.json is the only ground truth needed for scoring:

{"acceptable_pairs": [{"lon_action": "slow_down", "lat_action": "nudge_left"}]}

meta.json, abridged:

{"sample_id": "NP01_O10_center_v_full", "obstacle_type": "Pothole",
 "obstacle_category": "road_hazard", "position": "center", "navigation": "GO STRAIGHT"}

Usage 

hf download tangzx2024/CoLT-Drive --repo-type dataset --local-dir data

Inference and scoring instructions are in the code repository. Scoring uses an LLM judge; fix one judge model across every system you compare.

Limitations

This is an image-level, meta-action benchmark: no physical rollout, no trajectory, no closed-loop feedback. Accepted-set sizes vary by category, so compare within a category rather than across.

License

Apache License 2.0. The evaluation code is released under the same license.

Citation 

@inproceedings{tang2026coltdrive,
  title     = {CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction},
  author    = {Tang, Zhengxu and Cui, Guofeng and Gong, Ziyu and Zhang, Xiaozhou and Deng, Ruifeng and Qi, Chengzhi and Chen, Ke and Patil, Sachin and Xiao, Tianjun and Liu, Langechuan and Wang, Pichao},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2026},
  year      = {2026},
  url       = {https://arxiv.org/abs/2609.00242}
}
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