README.md

---
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
---

# 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.