Add Open-Vocabulary InstructNuPlan annotations (JSONL shards) and dataset card

README.md

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-# KAUST/InstructNuPlan
+---
+license: other
+license_name: imotion-llm-derived-annotations
+pretty_name: Open-Vocabulary InstructNuPlan
+task_categories:
+- text-generation
+- robotics
+language:
+- en
+tags:
+- autonomous-driving
+- motion-planning
+- instruction-following
+- nuplan
+- trajectory-generation
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: gpt_responses
+  data_files: gpt_responses-*.jsonl.gz
+- config_name: template_instructions
+  data_files: template_instructions-*.jsonl.gz
+- config_name: meta_prompts
+  data_files: meta_prompts-*.jsonl.gz
+- config_name: positive_prompts
+  data_files: gpt_positive_prompts-*.jsonl.gz
+- config_name: negative_prompts
+  data_files: gpt_negative_prompts-*.jsonl.gz
+---
 
-Write-access test. Real content and dataset card to follow.
+# Open-Vocabulary InstructNuPlan
+
+Safety-aware, open-vocabulary driving instruction annotations over **nuPlan**
+scenarios, from the WACV 2026 paper
+[**iMotion-LLM: Instruction-Conditioned Trajectory Generation**](https://openaccess.thecvf.com/content/WACV2026/html/Felemban_iMotion-LLM_Instruction-Conditioned_Trajectory_Generation_WACV_2026_paper.html).
+
+> **This dataset contains annotations only — no nuPlan sensor data, maps or logs.**
+> Every record is keyed to a nuPlan scenario token. To use it you must obtain
+> nuPlan yourself from <https://www.nuscenes.org/nuplan> under Motional's own
+> license and terms.
+
+## Why this exists
+
+The instruction/reasoning pairs here are **LLM-generated output**. They were
+produced by prompting a GPT model once, per scenario, and are therefore *not*
+reproducible by re-running the pipeline: generation is non-deterministic and
+costs API credits. Publishing them is the only way to reproduce the paper's
+open-vocabulary numbers exactly.
+
+Everything else in the paper's preprocessing chain **is** reproducible from code
+and is deliberately *not* published here — see "What is not here".
+
+## Coverage
+
+14 nuPlan scenario types:
+
+`accelerating_at_crosswalk`, `behind_bike`, `behind_long_vehicle`,
+`following_lane_with_lead`, `following_lane_without_lead`,
+`following_lane_with_slow_lead`, `starting_protected_cross_turn`,
+`starting_protected_noncross_turn`, `starting_unprotected_cross_turn`,
+`starting_unprotected_noncross_turn`, `stopping_with_lead`,
+`traversing_crosswalk`, `traversing_intersection`,
+`waiting_for_pedestrian_to_cross`
+
+## Configs
+
+| Config | Rows | What it is |
+|---|---|---|
+| `gpt_responses` | 7,119 | **The core dataset.** Per scenario: the prompt sent to the LLM and its returned instructions, each with `reasoning`, `safe` / `safe_instruction` flags and a `category` (e.g. "Without Context"). |
+| `template_instructions` | 88,133 | Template-rendered instruction/reasoning/decision records used to condition the trajectory model (`Instruction`, `Reasoning`, `Decision`, `Label`, `Direction_cls`). |
+| `meta_prompts` | 7,131 | Structured scenario metadata: `scenario_type`, `situation`, `what_safe_to_do`, `what_unsafe_to_do`. |
+| `positive_prompts` / `negative_prompts` | 7,131 each | Raw prompt text for the safe / unsafe instruction arms, kept for provenance. |
+
+Every row carries `_scene_id` (nuPlan token), `_source` (original relative path)
+and, where a source file held several records, `_line`.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("KAUST/InstructNuPlan", "gpt_responses", split="train")
+print(ds[0]["content"][0]["data"][0]["instructions"][0])
+```
+
+## Format note
+
+The original release was ~43,000 small `.txt` / `.json` files. They are packed
+here as gzipped JSONL so the dataset loads in one pass instead of tens of
+thousands of HTTP requests. `_source` preserves the original path of every
+record, so the on-disk layout is fully recoverable.
+
+## What is not here (reproducible from code)
+
+Deliberately omitted, because the released code regenerates it from nuPlan:
+
+- `npz` scenario tensor caches (~912 GiB)
+- rendered scenario figures (`.png`)
+
+See the [code repository](https://github.com/Vision-CAIR/iMotion-LLM) for the
+preprocessing and prompt-construction scripts.
+
+## Citation
+
+```bibtex
+@inproceedings{Felemban_2026_WACV,
+  author    = {Felemban, Abdulwahab and Hroub, Nussair and Ding, Jian and Abdelrahman, Eslam and Shen, Xiaoqian and Mohamed, Abduallah and Elhoseiny, Mohamed},
+  title     = {iMotion-LLM: Instruction-Conditioned Trajectory Generation},
+  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+  month     = {March},
+  year      = {2026},
+  pages     = {2710-2720}
+}
+```
+
+## Licensing
+
+These annotations are released for research use as a derived work. The
+underlying nuPlan data remains subject to Motional's license, which you must
+accept separately. Redistribution of nuPlan sensor data, maps or logs is **not**
+granted by this repository.

gpt_negative_prompts-00000.jsonl.gz

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gpt_positive_prompts-00000.jsonl.gz

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gpt_responses-00000.jsonl.gz

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+size 6226935

meta_prompts-00000.jsonl.gz

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+size 187638

template_instructions-00000.jsonl.gz

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+size 996024