Update README.md

README.md

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 ---
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc-by-4.0
+task_categories:
+  - multiple-choice
+language:
+  - en
+tags:
+  - autonomous-driving
+  - spatial-reasoning
+  - 3d-reasoning
+  - vqa
+pretty_name: Driving 3D Spatial Reasoning
+size_categories:
+  - n<1K
 dataset_info:
   features:
-  - name: id
-    dtype: string
-  - name: image
-    dtype: image
-  - name: category
-    dtype: string
-  - name: question
-    dtype: string
-  - name: choices
-    list: string
-  - name: answer
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 21337420
-    num_examples: 50
-  download_size: 21285474
-  dataset_size: 21337420
+    - name: id
+      dtype: string
+    - name: image
+      dtype: image
+    - name: category
+      dtype: string
+    - name: question
+      dtype: string
+    - name: choices
+      sequence: string
+    - name: answer
+      dtype: string
+---
+
+# Open Spatial Reasoning
+
+A multiple-choice dataset of **spatial reasoning questions and answers** for evaluating **3D spatial reasoning** from **single driving images**. Each image contains numbered bounding boxes referencing objects in the scene, and each question probes a model's ability to reconstruct the real 3D scene rather than rely on flat-image shortcuts (e.g. "lower in the frame = closer", "bigger box = nearer").
+
+## Dataset Description
+
+Frontier vision-language models often answer these questions correctly by luck while reasoning incorrectly, leaning on pixel-layout heuristics that break down on elevated roads, slopes, curves, and intersections. This dataset is designed to surface that failure mode by requiring metric 3D reasoning about distance, lateral position, ordering, and heading.
+
+Each sample pairs a driving-scene image with a question, four answer choices, and the correct answer letter.
+
+The images were collected by autonomous vehicles operated by **Plus AI**.
+
+### Data Fields
+
+| Field | Type | Description |
+|---|---|---|
+| `id` | `string` | Unique question identifier (e.g. `q0002`) |
+| `image` | `image` | The driving image with numbered bounding boxes |
+| `category` | `string` | The reasoning task type (see categories below) |
+| `question` | `string` | The full question, including the reasoning protocol |
+| `choices` | `list[string]` | Four answer options, prefixed `A.`–`D.` |
+| `answer` | `string` | The correct answer letter (`A`, `B`, `C`, or `D`) |
+
+### Question Categories
+
+The dataset spans several spatial-reasoning task types, including:
+
+| Category | What it tests |
+|---|---|
+| `identify_distance_long` | Estimate the absolute distance to an object (binned 0–20m / 20–50m / 50–80m / 80m+) |
+| `relative_distance_long` | Estimate the 3D separation between two objects |
+| `pick_closer` | Decide which of two objects is closer to the ego vehicle |
+| `identify_rightmost` | Identify the object furthest to the right in true 3D space |
+| `order_leftmost` | Order several objects left-to-right in 3D space |
+| `identify_position` | Classify an object's position relative to ego (e.g. ahead-left, behind-right) |
+| `identify_heading` | Determine an object's heading using clock directions (12 = forward, 3 = right) |
+
+## Authors
+
+Anurag Ganguli, [Anshuman Lall](https://huggingface.co/anshuman-reasoncore), Abhishek Bhatia, Xiangyu Gao, Joe Yuan, [Satish Vutukuru](https://huggingface.co/satishvutukuru), [Geoff Wolfe](https://huggingface.co/geoffwolfeRC)
+
+## Citation
+
+If you use this dataset, please cite it:
+
+```bibtex
+@misc{driving_3d_spatial_reasoning,
+  title  = {Open Spatial Reasoning},
+  author = {Anurag Ganguli, Anshuman Lall, Abhishek Bhatia, Xiangyu Gao, Joe Yuan, Satish Vutukuru, Geoff Wolfe},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/your-username/your-dataset-name}}
+}
+```
+
+## License
+
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Images were collected by autonomous vehicles operated by Plus AI.
 ---