Update README.md

README.md

@@ -1,16 +1,33 @@
 ---
-annotations_creators: []
-language: en
+annotations_creators:
+- expert-generated
+language:
+- en
+- es
+- zh
 size_categories:
 - n<1K
 task_categories:
-- image-classification
+- video-classification
 task_ids: []
-pretty_name: KITScenes-LongTail
+pretty_name: KITScenes-LongTail Videos
 tags:
 - fiftyone
 - group
-- image-classification
+- video
+- autonomous-driving
+- end-to-end-driving
+- trajectory-prediction
+- motion-planning
+- reasoning
+- chain-of-thought
+- multimodal
+- multilingual
+- long-tail
+- embeddings
+- depth-estimation
+- 3d-reconstruction
+- point-cloud
 dataset_summary: '
 
 
@@ -46,7 +63,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = load_from_hub("harpreetsahota/KITScenes-LongTail")
+  dataset = load_from_hub("Voxel51/KITScenes-LongTail")
 
 
   # Launch the App
@@ -58,13 +75,13 @@ dataset_summary: '
   '
 ---
 
-# Dataset Card for KITScenes-LongTail
-
-<!-- Provide a quick summary of the dataset. -->
-
+# Dataset Card for KITScenes-LongTail Videos
 
+![image/png](kitscenes_longtail.gif)
 
+A [FiftyOne](https://github.com/voxel51/fiftyone) **grouped video** dataset built from [KIT-MRT/KITScenes-LongTail](https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail), a long-tail autonomous-driving benchmark for VLMs/VLAs. 
 
+Each scenario is a ~9 s, six-camera surround-view event with a high-level driving instruction, multiple candidate future trajectories, and multilingual expert reasoning traces.
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 103 samples.
 
@@ -81,15 +98,28 @@ pip install -U fiftyone
 ```python
 import fiftyone as fo
 from fiftyone.utils.huggingface import load_from_hub
+from huggingface_hub import snapshot_download
+
+
+# Download the dataset snapshot to the current working directory
 
-# Load the dataset
-# Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/KITScenes-LongTail")
+snapshot_download(
+    repo_id="Voxel51/KITScenes-LongTail", 
+    local_dir=".", 
+    repo_type="dataset"
+    )
+
+# Load dataset from current directory using FiftyOne's native format
+dataset = fo.Dataset.from_dir(
+    dataset_dir=".",  # Current directory contains the dataset files
+    dataset_type=fo.types.FiftyOneDataset,  # Specify FiftyOne dataset format
+    name="KITScenes-LongTail"  # Assign a name to the dataset for identification
+)
 
 # Launch the App
 session = fo.launch_app(dataset)
-```
 
+```
 
 ## Dataset Details
 
@@ -97,21 +127,36 @@ session = fo.launch_app(dataset)
 
 <!-- Provide a longer summary of what this dataset is. -->
 
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
+KITScenes-LongTail targets generalization to rare ("long-tail") driving events for
+end-to-end driving. It provides synchronized multi-view video, ego trajectories,
+high-level instructions, and detailed multilingual reasoning traces, supporting
+in-context learning and few-shot generalization for multimodal models (VLMs and
+VLAs). Beyond safety/comfort metrics, it is designed to evaluate instruction
+following and the semantic coherence between a model's reasoning and its predicted
+trajectory. The reasoning traces are authored by domain experts (self-driving
+researchers) with diverse cultural backgrounds in English, Spanish, and Chinese.
+
+This FiftyOne version models each scenario as one **group** with six camera video
+slices, a composite surround slice, and a 3D point-cloud slice reconstructed from the
+front camera. The observed clip (~4 s) is real footage; where labels exist (train
+split), the 5 s prediction horizon is represented and the expert reasoning's action
+windows are stored as temporal detections (see **Dataset Structure** for the exact,
+faithful breakdown of what is verbatim vs. derived vs. synthetic). It also ships with
+ready-made saved views and Qwen3-VL clip embeddings (with similarity, 2D
+visualization, uniqueness, and representativeness indexes) for exploration.
+
+- **Curated by:** Institute of Measurement and Control Systems (MRT), Karlsruhe Institute of Technology (KIT), with collaborators from FZI Research Center for Information Technology, University Charles III of Madrid, Technical University of Madrid, University of Toronto, and Delft University of Technology.
+- **Funded by:** German Federal Ministry for Economic Affairs and Energy, project "NXT GEN AI METHODS"; compute provided by NHR@KIT (HoreKa).
+- **Shared by:** Original dataset by KIT-MRT; FiftyOne conversion published at `harpreetsahota/KITScenes-LongTail`.
+- **Language(s):** English, Spanish, Chinese (reasoning traces).
+- **License:** CC BY-NC 4.0, with additional dataset terms (non-commercial; cite the publication; anonymized faces/plates; data provided "as is"). Where the dataset terms conflict with CC BY-NC 4.0, the dataset terms prevail.
+
+### Dataset Sources
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/datasets/KIT-MRT/KITScenes-LongTail (original)
+- **Paper:** Wagner et al., "LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset," arXiv:2603.23607 (2026)
 
 ## Uses
 
@@ -121,19 +166,140 @@ session = fo.launch_app(dataset)
 
 <!-- This section describes suitable use cases for the dataset. -->
 
-[More Information Needed]
+- Benchmarking VLMs/VLAs for end-to-end driving in long-tail scenarios.
+- Trajectory prediction: forecasting a 5 s future trajectory (25 waypoints @ 5 Hz) from the past 4 s, the surround video, and a high-level instruction.
+- Instruction-following evaluation with fine-grained commands (e.g. "overtake truck driving on the right").
+- Studying semantic coherence between reasoning traces and predicted trajectories.
+- Zero-shot, few-shot, and few-shot chain-of-thought (CoT) prompting; multilingual reasoning research across English/Spanish/Chinese.
+- Multi-camera (360°) video understanding and qualitative review via the synchronized `surround` montage slice.
+- Dataset exploration with the bundled Qwen3-VL clip embeddings: similarity / nearest-neighbour search, 2D embedding plots, and uniqueness / representativeness ranking to surface the rarest or most prototypical scenarios.
+- Inspecting per-frame monocular **depth maps** and the merged front-camera **3D point cloud** (the `threed` slice) in the FiftyOne 3D viewer.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
 
-[More Information Needed]
+- Commercial use (the license is non-commercial).
+- Perception tasks needing ground-truth object annotations (2D/3D boxes, masks, tracks, depth, HD maps): none are provided. The `threed` point cloud and per-frame depth maps are **model-estimated** (VGGT-Omega), not sensor-measured ground truth, so they should not be used as depth/geometry labels.
+- Photometric or geometric tasks on the **synthetic future-horizon frames** of train videos (these freeze the last observed frame and are not real imagery).
+- Treating the held-out `test` split as labeled: its future trajectories and reasoning are withheld.
 
 ## Dataset Structure
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure. -->
+
+**Topology.** `media_type = group`. **103 groups** (3 `train` + 100 `test`),
+each with **8 group slices** (seven video, one 3D):
+
+- `front_left`, `front`, `front_right`, `rear_left`, `rear`, `rear_right` — the six surround cameras (one H.264 mp4 each).
+- `surround` — a derived 2×3 montage of the six cameras playing in sync (top row: front-left/front/front-right; bottom row: rear-left/rear/rear-right).
+- `threed` — a 3D point-cloud scene (`.fo3d`, `media_type = 3d`) reconstructed from the `front` clip, viewable in the FiftyOne 3D viewer (see **3D reconstruction** below).
+
+Each sample is also tagged with its split (`train` / `test`), and a `split` string
+field is stored for convenience.
+
+**Timeline.** Videos are 5 Hz. Frames `1..n_observed` (n_observed = 21, ~4 s) are the
+real observed clip. On `train`, frames after `n_observed` **freeze the last observed
+frame** to represent the 5 s prediction horizon, so the reasoning action windows can
+be stored as `TemporalDetections` with valid frame supports (train clips are 46
+frames; test clips are 21 frames, observation-only).
+
+**Sample fields**
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `scenario_id` | `StringField` | Scenario identifier, e.g. `"0000400"` (verbatim) |
+| `split` | `StringField` | `"train"` or `"test"` (also a sample tag) |
+| `camera` | `StringField` | Slice name for this sample (a camera, or `surround`) |
+| `fps` | `IntField` | Clip frame rate (5 Hz; from the paper) |
+| `n_observed_frames` | `IntField` | Number of real observed frames (21) |
+| `n_future_frames` | `IntField` | Synthetic future-horizon frames (25 on train, 0 on test) |
+| `now_frame` | `IntField` | 1-indexed frame marking "now" (the last observed frame) |
+| `driving_instruction` | `Classification` | High-level maneuver instruction (verbatim) |
+| `scenario_type` | `Classification` | Long-tail scenario category (verbatim) |
+| `trajectory_bev` | `DictField` | Ego BEV waypoints `[x, y]` (x forward, y left, meters): `past` (21 pts) plus populated future variants (verbatim values; `[[-100,-100]]` placeholders dropped) |
+| `reasoning_english` / `reasoning_spanish` / `reasoning_chinese` | `DictField` | The 9 expert reasoning fields per language, verbatim (train only) |
+| `driving_actions_english` / `_spanish` / `_chinese` | `TemporalDetections` | Acceleration/steering action windows (`0–3 s`, `3–5 s`); labels/reasons verbatim, frame supports derived (train only) |
+| `qa_english_reconstructed` | `ListField` | Reconstructed 5 Q&A pairs (paper's English question templates + verbatim answers) (train only) |
+| `reasoning_cot_english_reconstructed` | `StringField` | Reconstructed chain-of-thought block (train only) |
+| `scene_3d` | `StringField` | Path to the front-camera `.fo3d` 3D scene (also materialized as the `threed` slice) |
+| `qwen_emb_surround` / `qwen_emb_front` / `qwen_emb_rear` | `VectorField` | 2048-d Qwen3-VL clip embedding (one per slice; on the `surround` / `front` / `rear` slices respectively) |
+| `uniqueness_surround` / `uniqueness_front` / `uniqueness_rear` | `FloatField` | Per-clip uniqueness score in `[0, 1]` (higher = more unusual within its slice) |
+| `representativeness_surround` / `representativeness_front` / `representativeness_rear` | `FloatField` | Per-clip representativeness score in `[0, 1]` (higher = more prototypical within its slice) |
+
+**Frame fields** (attached to the six camera slices only; not the `surround` montage)
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `past_trail` | `Polylines` | History breadcrumb; grows over observed frames (per-frame ego-pose transform), then persists through the horizon |
+| `traj_expert_like` | `Polylines` | Expert (GT) future plan, progressively built out across the horizon |
+| `traj_wrong_speed` | `Polylines` | Adversarial future (wrong speed), built out across the horizon |
+| `traj_off_road` | `Polylines` | Adversarial future (off road), built out across the horizon |
+| `traj_crash` | `Polylines` | Adversarial future (crash), where present |
+| `plan_cursor` | `Keypoints` | Single marker advancing along the expert plan |
+
+Trajectories are rendered as `Polylines` (connected paths) rather than `Keypoints`
+because a driving trajectory is an ordered, connected curve; the advancing
+`plan_cursor` remains a single `Keypoint`.
+
+The **`front`** slice additionally carries per-frame outputs from the 3D
+reconstruction (sampled at 5 Hz):
+
+| Field | FiftyOne type | Description |
+|-------|---------------|-------------|
+| `depth_map` | `Heatmap` | Monocular depth map for the frame, rendered as an overlay in the App |
+| `camera_translation` | `ListField` | Camera position (3-vector) in the scene's OpenCV world frame |
+| `camera_rotation_matrix` | `ListField` | Camera rotation as a 3×3 matrix |
+| `camera_quaternion` | `ListField` | Camera rotation as a quaternion `[qw, qx, qy, qz]` |
+| `intrinsic_matrix` | `ListField` | Recovered 3×3 pinhole intrinsics for the frame |
+| `fov_h_deg` / `fov_w_deg` | `FloatField` | Estimated horizontal / vertical field of view, in degrees |
+
+**`dataset.info`**
+
+- `camera_parameters` — per-camera pinhole `K` and extrinsics `R`, `t` (ego→camera optical frame), copied verbatim from the dataset card; the rig is fixed so they apply to every sample.
+- `camera_parameters_note` — explanation of the calibration convention.
+- `timeline_note` — describes the 5 Hz timeline and the synthetic freeze-frame horizon.
+- `provenance` — explicit classification of every field as `verbatim_from_dataset`, `derived`, or `constructed_or_synthetic`, plus assumptions (`ground_z = -1.6 m`, `fps = 5 Hz`, synthetic horizon).
+
+**Parsing decisions**
+
+- **Verbatim:** `scenario_id`, `driving_instruction`, `scenario_type`, the per-language `reasoning_*` dicts, `trajectory_bev` waypoint values, and the real observed frames.
+- **Derived:** trajectory overlays are projected with the card's camera matrices plus a road-plane estimate `ground_z = -1.6 m` (and estimated per-frame headings for the past trail); `fps`/timing are taken from the paper (the parquet has no timestamps); temporal-detection frame supports map the paper's `0–3 s`/`3–5 s` windows onto the horizon.
+- **Constructed/synthetic:** the train future-horizon frames (a freeze of the last real frame); `qa_english_reconstructed` / `reasoning_cot_english_reconstructed` (assembled from paper question templates + verbatim answers). Spanish/Chinese Q&A are intentionally **not** fabricated.
+- **Held-out:** on `test`, future trajectories and all reasoning are withheld (empty), so those samples are inputs-only (video + instruction + scenario_type + past trajectory), have no future horizon, and no temporal detections.
+- Scenario-level labels are attached to every slice (including `surround`); per-pixel overlays are attached only to the camera slices (their coordinates are per-view, not montage-space).
+
+**Saved views**
+
+The dataset ships with these saved views (available from the view dropdown in the App
+or via `dataset.load_saved_view(...)`):
+
+| Saved view | Description |
+|------------|-------------|
+| `slice__front_left`, `slice__front`, `slice__front_right`, `slice__rear_left`, `slice__rear`, `slice__rear_right` | One per camera — flattens the group to a single camera so you can scan all 103 clips from that viewpoint |
+| `by_scenario_type_and_instruction` | The `surround` clips dynamically grouped by `scenario_type`, ordered by `driving_instruction` within each group |
+
+**Embeddings, similarity & quality scores**
+
+Each of the `surround`, `front`, and `rear` slices carries a **2048-d Qwen3-VL clip
+embedding** (`qwen_emb_<slice>`) summarizing the whole video clip, along with FiftyOne
+Brain indexes computed from those embeddings:
+
+| Brain key | Type | Use |
+|-----------|------|-----|
+| `sim_surround`, `sim_front`, `sim_rear` | similarity | Nearest-neighbour / sort-by-similarity search over clips |
+| `viz_surround`, `viz_front`, `viz_rear` | visualization (UMAP) | 2D embedding scatter plot for interactive exploration |
+| `uniqueness_surround`, `uniqueness_front`, `uniqueness_rear` | uniqueness | Per-clip `uniqueness_<slice>` score (find rare / outlier clips) |
+| `representativeness_surround`, `representativeness_front`, `representativeness_rear` | representativeness | Per-clip `representativeness_<slice>` score (find prototypical clips) |
+
+**3D reconstruction**
+
+The `threed` slice contains one `.fo3d` scene per scenario, reconstructed from the
+`front` clip with VGGT-Omega. Each scene holds a merged, colored point cloud plus
+camera frustums for the sampled frames and opens in the FiftyOne 3D viewer. The
+matching per-frame depth maps and camera poses live on the `front` slice's frame
+fields (see the frame-fields table above). The point cloud and depth are
+model-estimated (not sensor LiDAR), and are derived from the real observed frames.
 
 ## Dataset Creation
 
@@ -141,84 +307,98 @@ session = fo.launch_app(dataset)
 
 <!-- Motivation for the creation of this dataset. -->
 
-[More Information Needed]
+Generalization in perception has advanced, but decision-making in long-tail scenarios
+remains a major challenge. The dataset couples driving with high-level instructions
+and multilingual, step-by-step reasoning traces to accelerate progress on
+decision-making in rare events, and to enable studying how reasoning style and
+language affect driving competence. Multiple plausible maneuvers are evaluated rather
+than a single expert trajectory (the paper introduces a multi-maneuver score, MMS).
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+<!-- This section describes the source data. -->
 
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
+Recorded over two years beginning in late 2023 across urban, suburban, and highway
+environments (main locations: Karlsruhe, Heidelberg, Mannheim, and the Black Forest).
+Routes were adjusted to include construction zones and intersections, and filtered for
+rare events such as adverse weather (heavy rain, snow, fog), road closures, and
+accidents. Long-tail data was further selected using the Pareto principle with nuScenes
+as a reference distribution (80% cumulative-frequency threshold on rank-frequency
+plots). The full collection is 1,000 nine-second scenarios split train (500) / test
+(400) / validation (100); the released subset used here includes the test split and a
+small set of train samples. Each scenario provides synchronized six-view video over a
+360° horizontal field of view at 5 Hz, ~4 s observed, in raw, pinhole, and stitched
+formats (this FiftyOne build uses the pinhole frames, downscaled for encoding). The
+future prediction horizon is 5 s (25 waypoints at 5 Hz).
 
 #### Who are the source data producers?
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
+The KIT-MRT team and collaborators, recording with a research vehicle sensor suite.
 
-### Annotations [optional]
+### Annotations
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+<!-- If the dataset contains annotations which are not part of the initial data collection. -->
 
 #### Annotation process
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-
-[More Information Needed]
+High-level driving instructions were manually annotated by domain experts. Reasoning
+traces were collected by asking experts five questions per scenario: one open-ended
+situational-awareness question grounded in the instruction, and four questions about
+the acceleration and steering decisions for the next 0–3 s and the final 3–5 s of the
+expert trajectory. Action labels follow heuristics (acceleration: slight/strong
+accelerate, decelerate, maintain; steering: slight/sharp left/right, straight). Experts
+answered in their mother tongue (or a fluent language); verbal responses were
+transcribed with Whisper. For evaluation, reference trajectories are provided per
+scenario across categories (expert-like, wrong speed, neglect instruction, off road,
+crash); expert trajectories are driven, wrong-speed variants are augmented via state
+estimation and spline modification, and the remaining categories are manually labeled.
 
 #### Who are the annotators?
 
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Domain experts (researchers working on self-driving) with diverse linguistic and
+cultural backgrounds (English, Spanish, Chinese).
 
 #### Personal and Sensitive Information
 
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Faces and license plates are anonymized using state-of-the-art anonymization software
+(BrighterAI). Requests for removal of specific frames can be directed to
+info@mrt.kit.edu.
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+## Citation
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+<!-- If there is a paper or blog post introducing the dataset. -->
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{wagner2026longtaildrivingscenariosreasoning,
+  title={LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset},
+  author={Royden Wagner and Omer Sahin Tas and Jaime Villa and Felix Hauser and Yinzhe Shen and
+          Marlon Steiner and Dominik Strutz and Carlos Fernandez and Christian Kinzig and
+          Guillermo S. Guitierrez-Cabello and Hendrik Königshof and Fabian Immel and Richard Schwarzkopf and
+          Nils Alexander Rack and Kevin Rösch and Kaiwen Wang and Jan-Hendrik Pauls and Martin Lauer and
+          Igor Gilitschenski and Holger Caesar and Christoph Stiller},
+  year={2026},
+  eprint={2603.23607},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2603.23607},
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
+Wagner, R., Taş, Ö. Ş., Villa, J., Hauser, F., Shen, Y., Steiner, M., Strutz, D., Fernandez, C., Kinzig, C., Guitierrez-Cabello, G. S., Königshof, H., Immel, F., Schwarzkopf, R., Rack, N. A., Rösch, K., Wang, K., Pauls, J.-H., Lauer, M., Gilitschenski, I., Caesar, H., & Stiller, C. (2026). *LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset*. arXiv:2603.23607.
 
-[More Information Needed]
+## More Information
 
-## Dataset Card Authors [optional]
+The released subset contains the `test` split and a small number of `train` samples
+(few-shot examples); the full `train` and `validation` splits, along with stitched
+360° images, are announced for a later dataset version.
 
-[More Information Needed]
 
 ## Dataset Card Contact
 
-[More Information Needed]
+For the underlying dataset: info@mrt.kit.edu